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1
Gupta, Abhishek, and Ramesh Kumar Pachar. "A Hybrid Signal Processing Technique for Identification and Categorization of Faults in IEEE-9 Bus System." Advanced Engineering Forum 49 (May 31, 2023): 43–55. http://dx.doi.org/10.4028/p-jkw3p9.
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A hybrid signal processing technique (HSPT) is proposed in this manuscript for identification and categorization of faults in electrical transmission network. A fault indicator (FI) is suggested by decomposition of the currents by application of Alienation coefficient (ACF), Stockwell transform (ST) and Hilbert transform (HT) for identification of faults. An indicator for ground involvement during faulty condition (SGFI) is being suggested to detect the type of fault. The categorization of faults is done by utilizing faulty phase numbers and SGFI. It is found that the proposed technique is effective in identification of faults and to classify them in different scenarios together with fault on A-phase to ground (AGF), double phase fault (ABF), fault on two phases and ground (ABGF), three phase fault (ABCF) and three phase fault including ground (ABCGF). Study is done and validated on IEEE-9 bus system using MATLAB/Simulink environment. The effectiveness and applicability of the proposed technique with respect to different parameters of faults such as Fault Incidence Angle, Fault Impedance, Line loading, Generator Supply and Noise level is also checked. The results shows that proposed scheme is able to detect and classify the faults in different faulty events.
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Manjunatha, G., and H. C. Chittappa. "Bearing Fault Classification using Empirical Mode Decomposition and Machine Learning Approach." Journal of Mines, Metals and Fuels 70, no. 4 (2022): 214. http://dx.doi.org/10.18311/jmmf/2022/30060.
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Industrial machinery often breakdowns due to faults in rolling bearing. Bearing diagnosis plays a vital role in condition monitoring of machinery. Operating conditions and working environment of bearings make them prone to single or multiple faults. In this research, signals from both healthy and faulty bearings are extracted and decomposed into empirical modes. By analyzing different empirical modes from 8 derived empirical modes for healthy and faulty bearings under different fault sizes, the first mode has the most information to classify bearing condition. From the first empirical mode eight features in time domain were calculated for various bearing conditions like healthy, rolling element fault, outer and inner race fault. The feature extraction of vibration signal based on Empirical Mode Decomposition (EMD) is extensively explored and applied in diagnosis of fault in rolling bearings. This paper presents mathematical analysis for selection of valid Intrinsic Mode Functions (IMFs) of EMD. These chosen features are trained and classified using different classifiers. Among them K-star classifier is most reliable to categorize the bearing defects.
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Fang, Liang, and Hongchun Sun. "Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery." Applied Sciences 8, no. 9 (2018): 1441. http://dx.doi.org/10.3390/app8091441.
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A method is proposed to improve the feature extraction of vibration signals of rotating machinery. Firstly, the single-channel vibration signal is decomposed with ensemble empirical mode decomposition (EEMD). Then, the number of fault signals can be estimated with singular-value decomposition (SVD). Finally, the fault signals can be extracted with kernel-independent component analysis (KICA). The advantage of this method is that it can estimate the number of fault signals of single-channel vibration signals and can extract the fault features clearly. Compared with wavelets, empirical mode decomposition (EMD), variational mode decomposition (VMD) and EEMD, the better performance of this method is proven with three experimental analyses of faulty gear, a faulty rolling bearing and a faulty shaft. The results demonstrate that the proposed method is efficient to extract the fault features of single-channel vibration signals of rotating machinery.
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Zhang, Dingcheng, Dejie Yu, and Xing Li. "Optimal resonance-based signal sparse decomposition and its application to fault diagnosis of rotating machinery." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 24 (2016): 4670–83. http://dx.doi.org/10.1177/0954406216671542.
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The fault diagnosis of rotating machinery is quite important for the security and reliability of the overall mechanical equipment. As the main components in rotating machinery, the gear and the bearing are the most vulnerable to faults. In actual working conditions, there are two common types of faults in rotating machinery: the single fault and the compound fault. However, both of them are difficult to detect in the incipient stage because the weak fault characteristic signals are usually submerged by strong background noise, thus increasing the difficulty of the weak fault feature extraction. In this paper, a novel decomposition method, optimal resonance-based signal spares decomposition, is applied for the detection of those two types of faults in the rotating machinery. This method is based on the resonance-based signal spares decomposition, which can nonlinearly decompose vibration signals of rotating machinery into the high and the low resonance components. To extract the weak fault characteristic signals in the presence of strong noise effectively, the genetic algorithm is used to obtain the optimal decomposition parameters. Then, the optimal high and low resonance components, which include the fault characteristic signals of rotating machinery, can be obtained by using the resonance-based signal spares decomposition method with the optimal decomposition parameters. Finally, the high and the low resonance components are subject to the Hilbert transform demodulation analysis; the faults of rotating machinery can be diagnosed based on the obtained envelop spectra. The optimal resonance-based signal spares decomposition method is successfully applied to the analysis of the simulation and experiment vibration signals. The analysis results demonstrate that the proposed method can successfully extract the fault features in rotating machinery.
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Tong, Shuiguang, Yidong Zhang, Jian Xu, and Feiyun Cong. "Pattern recognition of rolling bearing fault under multiple conditions based on ensemble empirical mode decomposition and singular value decomposition." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 12 (2017): 2280–96. http://dx.doi.org/10.1177/0954406217715483.
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In rotating machinery, the malfunctions of rolling bearings are one of the most common faults. To prevent machine breakdown, the pattern recognition of rolling bearing faults has been a pivotal issue for fault identification and classification. This study proposes a new feature extraction method based on ensemble empirical mode decomposition (EEMD) and singular value decomposition (SVD) for fault classification. The proposed E–S method (EEMD combined with SVD using feature parameters) intends to enhance the faults identification capability in different working conditions, including various fault types (FT), fault severities (FS), and fault loads (FL). In this study, the E–S method is adopted to analyze the simulated signals. And the experiment further discusses three cases of different FT, FS, and FL separately under six different classifiers. The experimental results show that different fault classes can be effectively distinguished by the proposed E–S in comparison with other traditional feature extraction methods. Hence, the proposed method is verified to have an effective and excellent performance in bearing fault classification.
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Jing, Liuming, Lei Xia, Tong Zhao, and Jinghua Zhou. "An Improved Arc Fault Location Method of DC Distribution System Based on EMD-SVD Decomposition." Applied Sciences 13, no. 16 (2023): 9132. http://dx.doi.org/10.3390/app13169132.
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The influence of the control strategy of the power electronic converter obscures the fault characteristics of DC distribution networks. The existence of arc faults over an extended period of time poses a grave threat to the security of power grids and may result in electric shock, fire, and other catastrophes. In recent years, the method of fault localization based on the traveling wave method has been a popular topic of research in the field of DC distribution system protection. In this paper, the fault localization principle of the traveling wave method is described in depth, and the propagation characteristics of the traveling wave of fault current in the online mode network are deduced. We present a method for wave head calibration that combines empirical mode decomposition (EMD) and singular value decomposition (VMD). After the fault-traveling current signal has been subjected to EMD, the first eigenmode function is extracted and subjected to singular value decomposition (SVD). After SVD, the detail component can reflect the singularity of the signal. The point of the maximum value of the detail component signal corresponds to the moment when the faulty traveling wave head reaches the monitoring point. Finally, the DC distribution system is modeled based on the PSCAD/EMTDC simulation environment, and the fault location method is verified. The simulation results show that the method can effectively realize fault localization.
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Liao, Zhiqiang, Xuewei Song, Baozhu Jia, and Peng Chen. "Automatic Bearing Fault Feature Extraction Method via PFDIC and DBAS." Mathematical Problems in Engineering 2021 (May 25, 2021): 1–13. http://dx.doi.org/10.1155/2021/6655081.
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Determining the embedded dimension of a singular value decomposition Hankel matrix and selecting the singular values representing the intrinsic information of fault features are challenging tasks. Given these issues, this work presents a singular value decomposition-based automatic fault feature extraction method that uses the probability-frequency density information criterion (PFDIC) and dual beetle antennae search (DBAS). DBAS employs embedded dimension and singular values as dynamic variables and PFDIC as a two-stage objective to optimize the best parameters. The optimization results work for singular value decomposition for bearing fault feature extraction. The extracted fault signals combined with envelope demodulation can efficiently diagnose bearing faults. The superiority and applicability of the proposed method are validated by simulation signals, engineering signals, and comparison experiments. Results demonstrate that the proposed method can sufficiently extract fault features and accurately diagnose faults.
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Hu, Pan, Cunsheng Zhao, Jicheng Huang, and Tingxin Song. "Intelligent and Small Samples Gear Fault Detection Based on Wavelet Analysis and Improved CNN." Processes 11, no. 10 (2023): 2969. http://dx.doi.org/10.3390/pr11102969.
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Traditional methods for identifying gear faults typically require a substantial number of faulty samples, which in reality are challenging to obtain. To tackle this challenge, this paper introduces a sophisticated approach for intelligent gear fault identification, utilizing discrete wavelet decomposition and an enhanced convolutional neural network (CNN) optimized for scenarios with limited sample data. Initially, the features of the sample signal are extracted and enhanced using discrete wavelet decomposition. Subsequently, the refined signal is transformed into a two-dimensional image through a Markov transition field, preparing it for improved two-dimensional CNN training. Finally, the refined network model is applied to assess the gear fault dataset, achieving a training accuracy of 97% and a classification accuracy of 88.33%. This demonstrates the method’s feasibility and effectiveness in identifying gear faults with limited sample data.
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Dou, Chun Hong. "Fault Feature Extraction for Gearboxes Using Empirical Mode Decomposition." Advanced Materials Research 383-390 (November 2011): 1376–80. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1376.
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The paper uses empirical mode decomposition to extract the fault feature of gearboxes. Traditional techniques fail to process the non-stationary and nonlinear signals. Empirical mode decomposition is a powerful tool for the non-stationary and nonlinear signal analysis and has attracted considerable attention recently. First, a simulation signal is used to measure the performance of the empirical mode decomposition method. Then, the empirical mode decomposition method is applied to analyze the signals captured from the gearbox with multiple faults and successfully extracts the multiple fault information from the collected signals. The results show that empirical mode decomposition could be a helpful method for mechanical fault feature extraction.
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Zhao, Nanyang, Zhiwei Mao, Donghai Wei, Haipeng Zhao, Jinjie Zhang, and Zhinong Jiang. "Fault Diagnosis of Diesel Engine Valve Clearance Based on Variational Mode Decomposition and Random Forest." Applied Sciences 10, no. 3 (2020): 1124. http://dx.doi.org/10.3390/app10031124.
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Diesel engines, as power equipment, are widely used in the fields of the automobile industry, ship industry, and power equipment. Due to wear or faulty adjustment, the valve train clearance abnormal fault is a typical failure of diesel engines, which may result in the performance degradation, even valve fracture and cylinder hit fault. However, the failure mechanism features mainly in the time domain and angular domain, on which the current diagnosis methods are based, are easily affected by working conditions or are hard to extract accurate enough from, as the diesel engine keeps running in transient and non-stationary processes. This work aimed at diagnosing this fault mainly based on frequency band features, which would change when the valve clearance fault occurs. For the purpose of extracting a series of frequency band features adaptively, a decomposition technique based on improved variational mode decomposition was investigated in this work. As the connection between the features and the fault was fuzzy, the random forest algorithm was used to analyze the correspondence between features and faults. In addition, the feature dimension was reduced to improve the operation efficiency according to importance score. The experimental results under variable speed condition showed that the method based on variational mode decomposition and random forest was capable to detect the valve clearance fault effectively.
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Qiao, Hui, Bingxin Chen, Yaping Huang, Xuemei Qi, Hongming Fan, and Aiping Zeng. "A New Fault Recognition Method Based on Empirical Mode Decomposition and Texture Attributes." Elektronika ir Elektrotechnika 31, no. 1 (2025): 22–29. https://doi.org/10.5755/j02.eie.36989.
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Small faults developed in coal seams are one of the major causes of coal mine accidents. Accurately predicting small faults in coal fields is an urgent requirement for efficient and safe production in coal mines. This article proposes a new small fault identification method that combines the empirical mode decomposition method and the seismic texture attribute extraction method to address the problem of large errors caused by noise in the results of small fault prediction. Firstly, the basic principles of the empirical mode method and the texture attribute method were studied, and then the fault recognition ability of this method was tested and analysed based on a small fault seismic forward modelling. Meanwhile, empirical mode decomposition is performed on actual seismic data to identify small faults by using texture attributes and by adding noise to the seismic record; this article compared the seismic record of texture properties in the presence and absence of noise. The results indicate that the texture attribute method can predict small faults well, but this method is easily disturbed by noise. The empirical mode decomposition method used in this paper can remove noise interference and highlight characteristics of the texture attribute. Therefore, the small fault prediction method that combines empirical mode decomposition with texture attributes can effectively identify small faults and play an important geological guarantee role in ensuring safe and efficient production in coal mines.
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An, Xueli, Hongtao Zeng, and Chaoshun Li. "Envelope demodulation based on variational mode decomposition for gear fault diagnosis." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 231, no. 4 (2016): 864–70. http://dx.doi.org/10.1177/0954408916644271.
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A new time–frequency analysis method, based on variational mode decomposition, was investigated. When a gear fault occurs, its vibration signal is nonstationary, nonlinear, and exhibits complex modulation performance. According to the modulation characteristics of the gear vibration signal arising from faults therein, a gear fault diagnosis method based on variational mode decomposition and envelope analysis was proposed. The variational mode decomposition method can decompose a complex signal into several stable components. The obtained components were analyzed by envelope demodulation. According to the envelope spectrum, gear faults can be diagnosed. In essence, the variational mode decomposition method can decompose a multi-component signal into a number of single component amplitude modulation–frequency modulation signals. The method is suited to the handling of multi-component amplitude modulation–frequency modulation signals. The simulated signal and the actual gear fault vibration signals were analyzed. The results showed that the method can be effectively applied to gear fault diagnosis.
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Wang, Bing, Haihong Tang, Xiaojia Zu, and Peng Chen. "Adaptive Feature Extraction Using Sparrow Search Algorithm-Variational Mode Decomposition for Low-Speed Bearing Fault Diagnosis." Sensors 24, no. 21 (2024): 6801. http://dx.doi.org/10.3390/s24216801.
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To address the challenge of extracting effective fault features at low speeds, where fault information is weak and heavily influenced by environmental noise, a parameter-adaptive variational mode decomposition (VMD) method is proposed. This method aims to overcome the limitations of traditional VMD, which relies on manually set parameters. The sparrow search algorithm is used to calculate the fitness function based on mean envelope entropy, enabling the adaptive determination of the number of mode decompositions and the penalty factor in VMD. Afterward, the optimised parameters are used to enhance traditional VMD, enabling the decomposition of the raw signal to obtain intrinsic mode function components. The kurtosis criterion is then used to select relevant intrinsic mode functions for signal reconstruction. Finally, envelope analysis is applied to the reconstructed signal, and the results reveal the relationship between fault characteristic frequencies and their harmonics. The experimental results demonstrate that compared with other advanced methods, the proposed approach effectively reduces noise interference and extracts fault features for diagnosing low-speed bearing faults.
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Market, Saritha, Seenivasan Swaminathan, and Ravindranath Gurram. "LMD-based fault detection scheme for TCSC compensated wind integrated transmission lines." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 1 (2025): 26. http://dx.doi.org/10.11591/ijeecs.v37.i1.pp26-34.
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In this paper, a fast fault detection scheme is presented to detect the faults in thyristor-controlled series capacitor (TCSC) compensated transmission line connected with the large wind farms to export the electrical power to grid. The proposed logic utilizes the current information at the relay location and processes through the local mean decomposition technique to extract the magnitude features of the current. Cumulative sum of these features are computed for each phase currents to detect the faults in the transmission lines and further to classify the faulty phase in the system. The residual component of the current is used to detect the ground involvement in the faulty phase. The proposed method is tested during variety of faults by changing the nature of the fault using the fault parameters. Furthermore, the impact of the TCSC is also investigated along with the dynamic changes of the WF and their influence on the protection scheme. All the simulations are performed in MALTAB-Simulink software.
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Saritha, Market Seenivasan Swaminathan Ravindranath Gurram. "LMD-based fault detection scheme for TCSC compensated wind integrated transmission lines." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 1 (2025): 26–34. https://doi.org/10.11591/ijeecs.v37.i1.pp26-34.
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In this paper, a fast fault detection scheme is presented to detect the faults in thyristor-controlled series capacitor (TCSC) compensated transmission line connected with the large wind farms to export the electrical power to grid. The proposed logic utilizes the current information at the relay location and processes through the local mean decomposition technique to extract the magnitude features of the current. Cumulative sum of these features are computed for each phase currents to detect the faults in the transmission lines and further to classify the faulty phase in the system. The residual component of the current is used to detect the ground involvement in the faulty phase. The proposed method is tested during variety of faults by changing the nature of the fault using the fault parameters. Furthermore, the impact of the TCSC is also investigated along with the dynamic changes of the WF and their influence on the protection scheme. All the simulations are performed in MALTAB-Simulink software.
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Guo, Hao. "Fault Diagnosis of Gearbox Based on Complementary Ensemble Empirical Mode Decomposition and Kernel Fuzzy Clustering Algorithm." Journal of Big Data and Computing 2, no. 3 (2024): 24–28. https://doi.org/10.62517/jbdc.202401305.
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A bearing fault diagnosis method based on complementary ensemble empirical mode decomposition (EEMD) and kernel fuzzy c-means (KFCM) algorithm is proposed to address the difficulties in feature extraction and fault diagnosis of wind turbine gearbox vibration signals. Based on empirical mode decomposition method, complementary ensemble empirical mode decomposition is proposed for the decomposition of gearbox vibration signals, obtaining multiple intrinsic mode functions. By calculating the sample entropy of the intrinsic mode function components as feature vectors, the kernel fuzzy c-means algorithm is used to achieve gearbox fault diagnosis. The experimental results show that the proposed method can effectively identify gearbox faults. In order to verify the progressiveness of the proposed method, the proposed method is compared with other methods. The experimental results show that the proposed method has higher fault diagnosis accuracy, which verifies the progressiveness of the proposed method.
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Li, Weiguo, Naiyuan Fan, Xiang Peng, et al. "Fault Diagnosis for Motor Bearings via an Intelligent Strategy Combined with Signal Reconstruction and Deep Learning." Energies 17, no. 19 (2024): 4773. http://dx.doi.org/10.3390/en17194773.
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To overcome the incomplete decomposition of vibration signals in traditional motor-bearing fault diagnosis algorithms and improve the ability to characterize fault characteristics and anti-interference, a diagnostic strategy combining dual signal reconstruction and deep learning architecture is proposed. In this study, an improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD)-based signal reconstruction method is first introduced to extract features representing motor bearing faults. A feature matrix construction method based on improved information entropy is then proposed to quantify these fault features. Finally, a fault diagnosis algorithm architecture integrating a multi-scale convolutional neural network (MSCNN) with attention mechanisms and a bidirectional long short-term memory network (BiLSTM) is developed. The experimental results for four fault states show that this model can effectively extract fault features from original vibration signals and, compared to other fault diagnosis models, offer high diagnostic accuracy and strong generalization, maintaining high accuracy even under varying speeds and noise interference.
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Ao, HungLinh, Junsheng Cheng, Kenli Li, and Tung Khac Truong. "A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM." Shock and Vibration 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/825825.
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This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.
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Fu, Yanfang, Yu Ji, Gong Meng, Wei Chen, and Xiaojun Bai. "Three-Phase Inverter Fault Diagnosis Based on an Improved Deep Residual Network." Electronics 12, no. 16 (2023): 3460. http://dx.doi.org/10.3390/electronics12163460.
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This study addresses the challenges of limited fault samples, noise interference, and low accuracy in existing fault diagnosis methods for three-phase inverters under real acquisition conditions. To increase the number of samples, Wavelet Packet Decomposition (WPD) denoising and a Conditional Variational Auto-Encoder (CVAE) are used for sample enhancement based on the existing faulty samples. The resulting dataset is then normalized, pre-processed, and used to train an improved deep residual network (SE-ResNet18) fault diagnosis model with a channel attention mechanism. Results show that the augmented fault samples improve the diagnosis accuracy compared with the original samples. Furthermore, the SE-ResNet18 model achieves higher fault diagnosis accuracy with fewer iterations and faster convergence, indicating its effectiveness in accurately diagnosing inverter open-circuit faults across various sample situations.
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Gan, Weiwei, Xueming Li, Dong Wei, Rongjun Ding, Kan Liu, and Zhiwen Chen. "Real-Time Multi-Sensor Joint Fault Diagnosis Method for Permanent Magnet Traction Drive Systems Based on Structural Analysis." Sensors 24, no. 9 (2024): 2878. http://dx.doi.org/10.3390/s24092878.
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Sensor faults are one of the most common faults that cause performance degradation or functional loss in permanent magnet traction drive systems (PMTDSs). To quickly diagnose faulty sensors, this paper proposes a real-time joint diagnosis method for multi-sensor faults based on structural analysis. Firstly, based on limited monitoring signals on board, a structured model of the system was established using the structural analysis method. The isolation and detectability of faulty sensors were analyzed using the Dulmage–Mendelsohn decomposition method. Secondly, the minimum collision set method was used to calculate the minimum overdetermined equation set, transforming the higher-order system model into multiple related subsystem models, thereby reducing modeling complexity and facilitating system implementation. Next, residual vectors were constructed based on multiple subsystem models, and fault detection and isolation strategies were designed using the correlation between each subsystem model and the relevant sensors. The validation results of the physical testing platform based on online fault data recordings showed that the proposed method could achieve rapid fault detection and the localization of multi-sensor faults in PMTDS and had a good application value.
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Chen, Shengbin. "Line fault location of flexible DC distribution network based on adaptive noise empirical mode decomposition." Journal of Physics: Conference Series 2683, no. 1 (2024): 012037. http://dx.doi.org/10.1088/1742-6596/2683/1/012037.
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Abstract A fault location method for flexible DC distribution network lines is proposed to address the issues of insufficient decomposition in fault signal extraction and the complexity of traveling wave head detection. The method combines adaptive noise empirical mode decomposition (CEEMDAN) and Teager energy operator (TEO). Firstly, CEEMDAN is used to adaptively denoise the line mode signal and obtain the optimal modal component to overcome modal aliasing and insufficient decomposition in signal separation. Then, the reach time of the fault traveling wave is determined by using TEO. Ultimately, the fault location is accurately determined by employing both ends of the traveling wave ranging technique. Through the combination of software PSCAD and MATLAB, the simulation experiment of the line fault is carried out. This means that the method can quickly and precisely identify the location of faults in a system.
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Wang, Xiaoqian, Dali Sheng, Jinlian Deng, et al. "Kernel Regression Residual Decomposition Method to Detect Rolling Element Bearing Faults." Mathematical Problems in Engineering 2021 (April 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/5523098.
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The raw vibration signal carries a great deal of information representing the mechanical equipment's health conditions. However, in the working condition, the vibration response signals of faulty components are often characterized by the presence of different kinds of impulses, and the corresponding fault features are always immersed in heavy noises. Therefore, signal denoising is one of the most important tasks in the fault detection of mechanical components. As a time-frequency signal processing technique without the support of the strictly mathematical theory, empirical mode decomposition (EMD) has been widely applied to detect faults in mechanical systems. Kernel regression (KR) is a well-known nonparametric mathematical tool to construct a prediction model with good performance. Inspired by the basic idea of EMD, a new kernel regression residual decomposition (KRRD) method is proposed. Nonparametric Nadaraya–Watson KR and a standard deviation (SD) criterion are employed to generate a deep cascading framework including a series of high-frequency terms denoted by residual signals and a final low-frequency term represented by kernel regression signal. The soft thresholding technique is then applied to each residual signal to suppress noises. To illustrate the feasibility and the performance of the KRRD method, a numerical simulation and the faulty rolling element bearings of well-known open access data as well as the experimental investigations of the machinery simulator are performed. The fault detection results show that the proposed method enables the recognition of faults in mechanical systems. It is expected that the KRRD method might have a similar application prospect of EMD.
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Zhou, Qi, Chenyu Zhang, and Ruihuang Liu. "Open-circuit fault diagnosis of AC port of energy router based on wavelet packet decomposition and gradient boosting decision trees." Journal of Physics: Conference Series 2936, no. 1 (2025): 012026. https://doi.org/10.1088/1742-6596/2936/1/012026.
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Abstract The energy routers have become the key devices in smart grids. The open-circuit (OC) fault diagnosis is one of the common faults for the AC port of the energy router. In this paper, an OC fault diagnosis of the AC port of an energy router, i.e., a 3-level NPC inverter, based on wavelet packet decomposition and gradient boosting decision trees is proposed. The wavelet packet decomposition is used to extract the fault features from the sampled 3-phase current waveforms. Then, the fault classification model based on gradient boosting decision trees (GBDT) is built to diagnose the fault localization of power switch devices. Simulation results show that the original current signal can be decomposed into low and high-frequency components effectively. The trained GBDT model can effectively diagnose OC faults in different fault localizations of the AC port of the energy router.
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Li, Hui. "Fault Diagnosis of Gear Wear Based on Local Mean Decomposition." Advanced Materials Research 459 (January 2012): 298–302. http://dx.doi.org/10.4028/www.scientific.net/amr.459.298.
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A new approach to fault diagnosis of gear wear based on Local mean decomposition (LMD) is proposed. Local mean decomposition can adaptively decomposes the vibration signal into a series of product functions (PFs), which is the product of an envelope signal and a frequency modulated signal. LMD is capable of revealing interesting feature embedded in the signal. The experimental examples are conducted to evaluate the effectiveness of the proposed approach. The experimental results provide strong evidence that the performance of the approach based on local mean decomposition is better to extract the fault characteristics of the faulty gear and can effectively diagnose the gear wear fault.
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Liu, Xinyue, Yan Yan, Kaibo Hu, et al. "Fault Diagnosis of Rotor Broken Bar in Induction Motor Based on Successive Variational Mode Decomposition." Energies 15, no. 3 (2022): 1196. http://dx.doi.org/10.3390/en15031196.
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When an induction motor is running at stable speed and low slip, the fault signal of the induction motor’s broken bar faults are easily submerged by the power frequency (50 Hz) signal. Thus, it is difficult to extract fault characteristics. The left-side harmonic component representing the fault characteristics can be distinguished from power frequency owing to V-shaped trajectory of the fault component in time-frequency (t-f) domain during motor startup. This article proposed a scheme to detect broken bar faults and discriminate the severity of faults under starting conditions. In this scheme, successive variable mode decomposition (SVMD) is applied to analyze the stator starting current to extract the fault component, and the signal reconstruction is proposed to maximize the energy of the fault component. Then, the quadratic regression curve method of instantaneous frequency square value of the fault component is utilized to discriminate whether the fault occurs. In addition, according to the feature that the energy of the fault component increases with the fault severity, the energy of the right part of the fault component is proposed to detect the severity of the fault. In this paper, experiments are carried out based on a 5.5 kW three-pole induction motor. The results show that the scheme proposed in this paper can diagnose the broken bar faults and determine the severity of the fault.
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An, Xueli, and Luoping Pan. "Bearing fault diagnosis of a wind turbine based on variational mode decomposition and permutation entropy." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 231, no. 2 (2017): 200–206. http://dx.doi.org/10.1177/1748006x17693492.
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Variational mode decomposition is a new signal decomposition method, which can process non-linear and non-stationary signals. It can overcome the problems of mode mixing and compensate for the shortcomings in empirical mode decomposition. Permutation entropy is a method which can detect the randomness and kinetic mutation behavior of a time series. It can be considered for use in fault diagnosis. The complexity of wind power generation systems means that the randomness and kinetic mutation behavior of their vibration signals are displayed at different scales. Multi-scale permutation entropy analysis is therefore needed for such vibration signals. This research investigated a method based on variational mode decomposition and permutation entropy for the fault diagnosis of a wind turbine roller bearing. Variational mode decomposition was adopted to decompose the bearing vibration signal into its constituent components. The components containing key fault information were selected for the extraction of their permutation entropy. This entropy was used as a bearing fault characteristic value. The nearest neighbor algorithm was employed as a classifier to identify faults in a roller bearing. The experimental data showed that the proposed method can be applied to wind turbine roller bearing fault diagnosis.
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Xu, Zhenzhong, Xu Chen, Linchao Yang, Jiangtao Xu, and Shenghan Zhou. "Multi-modal adaptive feature extraction for early-stage weak fault diagnosis in bearings." Electronic Research Archive 32, no. 6 (2024): 4074–95. http://dx.doi.org/10.3934/era.2024183.
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We present a novel multi-modal adaptive feature extraction algorithm considering both time-domain and frequency-domain modalities (AFETF), coupled with a Bidirectional Long Short-Term Memory (Bi-LSTM) network based on the Grey Wolf Optimizer (GWO) for early-stage weak fault diagnosis in bearings. Singular Value Decomposition (SVD) was employed for noise reduction, while Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was utilized for signal decomposition, facilitating further signal processing. AFETF algorithm proposed in this paper was employed to extract weak fault features. The adaptive diagnostic process was further enhanced using Bi-LSTM network optimized with GWO, ensuring objectivity in the hyperparameter optimization. The proposed method was validated for datasets containing weak faults with a 0.2 mm crack and strong faults with a 0.4 mm crack, demonstrating its effectiveness in early-stage fault detection.
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Zhang, Decai, Xueping Ren, and Hanyue Zuo. "Compound Fault Diagnosis for Gearbox Based Using of Euclidean Matrix Sample Entropy and One-Dimensional Convolutional Neural Network." Shock and Vibration 2021 (April 10, 2021): 1–26. http://dx.doi.org/10.1155/2021/6669006.
Full textAbstract:
Vibration signals of gearbox under different loads are sensitive to the existence of the fault and composite fault vibration signals are complex. Traditional fault diagnosis methods mostly rely on signal processing methods. It is difficult for signal processing methods to separate effective information from those fault signals. Therefore, traditional fault diagnosis methods are difficult to accurately identify those faults. In this paper, a one-dimensional convolutional neural network (1-D CNN) intelligent diagnosis method with improved SoftMax function is proposed. Local mean decomposition (LMD) decomposes the signals into different physical fictions (PF). PFs are input into the matrix sample entropy based on Euclidean distance (MESE), and the PFs which best reflect fault characteristics are selected. Finally, the PFs by MESE are used to train the CNN to identify the faults of parallel-shaft gearbox. Experiment shows that MESE can quickly and accurately select the PFs with the most significant fault features. 1-D CNN can get nearly 100% recognition rate with less time and the CNN of SoftMax improved can effectively eliminate LMD endpoint effect. This method can successfully identify single faults, combination faults, and faults under different loads of the gearbox. Compared with other methods, this method has the characteristics of high efficiency, accuracy, and strong anti-interference. Therefore, it can effectively solve the problem of complex fault signal decomposition of gearbox and can diagnose the gearbox fault under different load operation. It has great significance for gearbox fault diagnosis in actual production.
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Wang, HongChao, and WenLiao Du. "Intelligent diagnosis of rolling bearing compound faults based on device state dictionary set sparse decomposition feature extraction–hidden Markov model." Advances in Mechanical Engineering 12, no. 6 (2020): 168781402093046. http://dx.doi.org/10.1177/1687814020930469.
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Identification of rolling bearing fault patterns, especially for the compound faults, has attracted notable attention and is still a challenge in fault diagnosis. Intelligent diagnosis method is an effective method for compound faults of rolling element bearing, and effective fault feature extraction is the key step to decide the intelligent diagnosis result to some extent. The sparse decomposition method could capture the complex impulsive characteristic components of rolling bearing more effectively than the other time–frequency analysis method when compound fault arises in rolling bearing. Based on the self-learning dictionary under different operating states of the device corresponding to the special features modes, an intelligent diagnosis method of rolling bearing compound faults based on device state dictionary set sparse decomposition feature extraction–hidden Markov model is proposed in the article. First, characteristic dictionaries of rolling bearing under different operating conditions are extracted by sparse decomposition self-learning method, and state dictionary set of rolling bearing is constructed. Then, the compound fault signals of bearing are transformed into sparse domain using the constructed dictionary set to extract sparse features. At last, the extracted sparse features are used as training and testing vectors of hidden Markov model, and satisfactory intelligent diagnosis results are obtained. The validity of the proposed method is verified by compound faults of rolling element bearing. In addition, the advantages of the proposed method are also verified by comparing with the other feature extraction and intelligent diagnosis methods, and the proposed method provides a feasible and efficient solution for fault diagnosis of rolling bearing compound faults.
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Wang, Baoxiang, Guoqing Liu, Jihai Dai, and Chuancang Ding. "Improved Variational Mode Decomposition Based on Scale Space Representation for Fault Diagnosis of Rolling Bearings." Sensors 25, no. 11 (2025): 3542. https://doi.org/10.3390/s25113542.
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Accurate extraction of weak fault information from non-stationary vibration signals collected by vibration sensors is challenging due to severe noise and interference. While variational mode decomposition (VMD) has been effective in fault diagnosis, its reliance on predefined parameters, such as center frequencies and mode number, limits its adaptability and performance across different signal characteristics. To address these limitations, this paper proposes an improved variational mode decomposition (IVMD) method that enhances diagnostic performance by adaptively determining key parameters based on scale space representation. In concrete, the approach constructs a scale space by computing the inner product between the signal’s Fourier spectrum and a Gaussian function, and then identifies both the mode number and initial center frequencies through peak detection, ensuring more accurate and stable decomposition. Moreover, a multipoint kurtosis (MKurt) criterion is further employed to identify fault-relevant components, which are then merged to suppress redundancy and enhance diagnostic clarity. Experimental validation on locomotive bearings with inner race faults and compound faults demonstrates that IVMD outperforms conventional VMD by effectively extracting fault features obscured by noise. The results confirm the robustness and adaptability of IVMD, making it a promising tool for fault diagnosis in complex industrial environments.
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Cui, Hongyu, Yuanying Qiao, Yumei Yin, and Ming Hong. "An investigation on early bearing fault diagnosis based on wavelet transform and sparse component analysis." Structural Health Monitoring 16, no. 1 (2016): 39–49. http://dx.doi.org/10.1177/1475921716661310.
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Rolling bearings, as important machinery components, strongly affect the operation of machines. Early bearing fault diagnosis methods commonly take time–frequency analysis as the fundamental basis, therein searching for characteristic fault frequencies based on bearing kinematics to identify fault locations. However, due to mode mixing, the characteristic frequencies are usually masked by normal frequencies and thus are difficult to extract. After time–frequency decomposition, the impact signal frequency can be distributed among multiple separation functions according to the mode mixing caused by the impact signal; therefore, it is possible to search for the shared frequency peak value in these separation functions to diagnose bearing faults. Using the wavelet transform, time–frequency analysis and blind source separation theory, this article presents a new method of determining shared frequencies, followed by identifying the faulty parts of bearings. Compared to fast independent component analysis, the sparse component analysis was better able to extract fault characteristics. The numerical simulation and the practical application test in this article obtained satisfactory results when combining the wavelet transform, intrinsic time-scale decomposition and linear clustering sparse component analysis, thereby proving the validity of this method.
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Akolkar, S. M., and H. R. Jariwala. "Highly efficient relay triggering circuit for fault detection during Power swings." Journal of Applied Research and Technology 22, no. 1 (2024): 52–58. http://dx.doi.org/10.22201/icat.24486736e.2024.22.1.2120.
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This paper introduces a Discrete Wavelet Transform based very simple and fast acting algorithm with multi-resolution analysis to sense all types of faults in presence of power swings using current signal analyzation. The algorithm confirms very quick and efficient detection of various fault types in the first signal decomposition level of signal. The novelty of proposed algorithm lies in use of special type of Battle Lemarie mother wavelet having an advantage of perfect symmetry ensuring decomposition into B-Spline or same order polynomials capturing excellent speed and time-frequency localization of signal. The algorithm is tested for different fault parameters such as fault resistance, fault distance and time of initiation of faults considering EHV double circuit transmission line network and IEEE 9 Bus system developed in MATLAB environment. The proposed algorithm is capable of detecting all types of faults consistently within minimum time of 0.001 sec.
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Chen, Yong Hui, Xue Liang Zhang, and Hai Hong Li. "Feature Extraction of Nonstationarity Vibration Signal Based on Wavelet Decomposition." Applied Mechanics and Materials 220-223 (November 2012): 2228–34. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2228.
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Abstract. Nonstationarity feature representation and extraction method based on the wavelet decomposition and demodulation techniques are studied. Some component in special frequency band included faulty information is selected to reconstruct by wavelet analysis. The mono-components with fault feature in different frequency band would be captured and separated out. The demodulated and spectrally signals are analyzed by Hilbert transform, and it presents an approach to get the characteristic frequency of fault signals. So what kind of the fault mode is can be estimated. For the nonstationarity and modulation feature of rolling bearing fault signals, wavelet decomposition combined with Hilbert transform is effective in identifying the localized defects of rolling bearings.
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Kedadouche, Mourad, and Zhaoheng Liu. "Fault feature extraction and classification based on WPT and SVD: Application to element bearings with artificially created faults under variable conditions." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 22 (2016): 4186–96. http://dx.doi.org/10.1177/0954406216663782.
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Achieving a precise fault diagnosis for rolling bearings under variable conditions is a problematic challenge. In order to enhance the classification and achieves a higher precision for diagnosing rolling bearing degradation, a hybrid method is proposed. The method combines wavelet packet transform, singular value decomposition and support vector machine. The first step of the method is the decomposition of the signal using wavelet packet transform and then instantaneous amplitudes and energy are computed for each component. The Second step is to apply the singular value decomposition to the matrix constructed by the instantaneous amplitudes and energy in order to reduce the matrix dimension and obtaining the fault feature unaffected by the operating condition. The features extracted by singular value decomposition are then used as an input to the support vector machine in order to recognize the fault mode of rolling bearings. The method is applied to a bearing with faults created using electro-discharge machining under laboratory conditions. Test results show that the proposed methodology is effective to classify rolling bearing faults with high accuracy.
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Zhu, Wenwei, Chenyang Fan, Chenghao Xu, et al. "Anchor Fault Identification Method for High-Voltage DC Submarine Cable Based on VMD-Volterra-SVM." Energies 16, no. 7 (2023): 3053. http://dx.doi.org/10.3390/en16073053.
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This article introduces a new method for identifying anchor damage faults in fiber composite submarine cables. The method combines the Volterra model of Variation Mode Decomposition (VMD) with singular value entropy to improve the accuracy of fault identification. First, the submarine cable vibration signal is decomposed into various Intrinsic Mode Functions (IMFs) using VMD. Then, a Volterra adaptive prediction model is established by reconstructing the phase space of each IMF, and the model parameters are used to form an initial feature vector matrix. Next, the feature vector matrix is subjected to singular value decomposition to extract the singular value entropy that reflects the fault characteristics of the submarine cable. Finally, singular value entropy is used as a feature value to input into the Support Vector Machine (SVM) for classification. Compared with Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD), the proposed method achieves a higher fault identification accuracy and effectively identifies anchor damage faults in submarine cables. The results of this study demonstrate the feasibility and practicality of the proposed method.
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Li, Hui. "Local Mean Decomposition Based Bearing Fault Detection." Advanced Materials Research 490-495 (March 2012): 360–64. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.360.
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A novel method of bearing fault diagnosis based on local mean decomposition (LMD) is proposed. LMD method is self-adaptive to non-stationary and non-linear signal. LMD can adaptively decompose the vibration signal into a series of product functions (PFs), which is the product of an envelope signal and a frequency modulated signal. Then the envelope spectrum is applied to the selected product function that stands for the bearing faults. Therefore, the character of the bearing fault can be recognized according to the envelope spectrum of product function. The experimental results show that local mean decomposition based envelope spectrum can effectively detect and diagnose bearing inner and outer race fault under strong background noise condition.
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Türkmenoğlu, Veli, Mustafa Aktaş, Serkan Karataş, and Halil İbrahim Okumuş. "Soft Set-Based Switching Faults Decision Making in DTC Induction Motor Drives." Journal of Circuits, Systems and Computers 24, no. 02 (2014): 1550021. http://dx.doi.org/10.1142/s0218126615500218.
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This paper introduces a method for detection and identification of IGBT-based drive open-circuit fault of DTC induction motor drives. The detection mechanism is based on soft set theory and wavelet decomposition, if it is detailed, ⊼-product decision making method and sym2 wavelet decomposition have been used in the detection mechanism. In this method, the stator currents have been used as an input to the system. The stator current has been used for the detection of the fault. The signal analysis has been performed up to the six level details wavelets decomposition. Faulty switch is detected by applying soft set theory to sixth level wavelets transformation. This is the first time applied to inverter in induction motor drives fault detection. The results demonstrate that the proposed fault detection and diagnosis system has very good capabilities.
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Gao, Shuzhi, Ning Zhao, Xuefeng Chen, Zhiming Pei, and Yimin Zhang. "A new approach to adaptive VMD based on SSA for rolling bearing fault feature extraction." Measurement Science and Technology 35, no. 3 (2023): 036102. http://dx.doi.org/10.1088/1361-6501/ad11cc.
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Abstract Due to the structure of rolling bearings, will have various problems. So the early detection of rolling bearing faults is very important. Consequently, a precise method for extracting fault features is required. In this study, an adaptive variational modal decomposition (VMD) fault feature extraction method is proposed, utilizing the sparrow search algorithm (SSA). Firstly, a novel measurement index called impulse diversity entropy (IDE) is introduced, which better represents internal changes within the mode components. Secondly, the SSA is employed to select the optimal VMD decomposition parameters based on the IDE. Finally, a spectrum analysis is conducted on the mode component with the highest IDE to extract fault features. The experimental results show that this method has an accurate feature extraction ability and obvious advantages over other methods in distinguishing fault and interference frequencies because it is a special signal decomposition method.
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Li, Quanfu, Yuxuan Zhou, Gang Tang, Chunlin Xin, and Tao Zhang. "Early Weak Fault Diagnosis of Rolling Bearing Based on Multilayer Reconstruction Filter." Shock and Vibration 2021 (March 2, 2021): 1–15. http://dx.doi.org/10.1155/2021/6690966.
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The early weak fault characteristics of rolling bearings are extremely weak and are easily overwhelmed by other noises. In order to effectively extract the characteristics of the early weak faults of the rolling bearings and draw on the multilayer wavelet decomposition idea, a method for diagnosing the early weak faults of the rolling bearing based on the multilayer reconstruction filter is proposed. As we all know, empirical wavelet transform (EWT) makes full use of wavelet filter bank, and variational mode decomposition (VMD) uses Wiener filter bank. This paper fully combines the advantages of the above two methods, adaptively determines the number of modes through empirical wavelet decomposition and divides the original signal, extracts the frequency band that contains the fault characteristic information, and effectively eliminates noise interference. These steps are repeated until the optimal component of the condition is obtained. In the output layer, the weak fault impact components are further separated by the strong filtering and signal decomposition capability of VMD. The advantages of the proposed method are proved by the experiment of weak fault of rolling bearing and the accelerated failure experiment of full life. The proposed method has the advantages of reducing noise influence and adaptive estimation of decomposed modes, which can be applied more efficiently in practice.
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Xi, Wei, Fuyu Qiao, and Jingjing Zhang. "A novel rolling bearing fault detect method based on feature mode decomposition and subtraction-average-based optimizer." PLOS One 20, no. 6 (2025): e0324739. https://doi.org/10.1371/journal.pone.0324739.
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Large rotating machinery is an essential piece of equipment in modern industry, playing a critical role in industrial production. However, the complex working environment complicates the extraction of fault-related information. This paper proposes a fault diagnosis method based on the subtraction-average-based optimizer (SABO) and feature mode decomposition (FMD). To address the issue that FMD’s decomposition performance is highly sensitive to its parameter settings, this paper uses the minimum envelope entropy as the fitness function and employs the SABO algorithm to adaptively optimize FMD’s two key parameters: the mode number (n) and filter length (L). Additionally, for the intrinsic mode functions (IMFs) obtained from FMD decomposition, the maximum kurtosis value is used to filter IMFs containing fault information, and envelope spectrum analysis is applied to achieve fault diagnosis. When applied to experimental signals of rolling bearing faults, the results demonstrate that the proposed method can extract the amplitude of the fault characteristic frequency from the envelope spectrum and accurately diagnose the fault type. Compared with methods based on empirical mode decomposition (EMD) and fixed-parameter FMD, the proposed method provides a more prominent representation of the fault characteristic frequency and its harmonics in the envelope spectrum. Furthermore, the proposed method achieves a more prominent representation of the fault eigenfrequency in the envelope spectrum and a lower error rate. The proposed method demonstrates significant potential and value for rolling bearing fault diagnosis.
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Cao, Li, and Wenlei Sun. "Research on Bearing Fault Identification of Wind Turbines’ Transmission System Based on Wavelet Packet Decomposition and Probabilistic Neural Network." Energies 17, no. 11 (2024): 2581. http://dx.doi.org/10.3390/en17112581.
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In order to improve the reliability and life of the wind turbine, this paper takes the rolling bearing in the experimental platform of the wind turbine as the research object. In order to obtain the intrinsic mode function (IMF) of each fault type, the original signals of different fault states of the rolling bearing on the experimental platform are decomposed by using the overall average empirical mode decomposition method (EEMD) and the wavelet packet decomposition method (WPD), respectively. Then the energy ratio of the IMF component of the different types of faults to the total energy value is calculated and the eigenvectors of different types of faults are constructed. The extreme learning machine (ELM) and probabilistic neural network (PNN) are used to learn fault types and eigenvector samples to identify the faults of the rolling bearing. It is found that the bearing fault characteristics obtained by the WPD method are more obvious, and the results obtained by the same recognition method are ideal; and the PNN method is obviously superior to the extreme learning machine method in bearing fault recognition rate.
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Shi, Luojie, Juan Wen, Baisong Pan, Yongyong Xiang, Qi Zhang, and Congkai Lin. "Dynamic Characteristics of a Gear System with Double-Teeth Spalling Fault and Its Fault Feature Analysis." Applied Sciences 10, no. 20 (2020): 7058. http://dx.doi.org/10.3390/app10207058.
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Tooth spalling is one of the most destructive surface failure models of the gear faults. Previous studies have mainly concentrated on the spalling damage of a single gear tooth, but the spalling distributed over double teeth, which usually occurs in practical engineering problems, is rarely reported. To remedy this deficiency, this paper constructs a new dynamical model of a gear system with double-teeth spalling fault and validates this model with various experimental tests. The dynamic characteristics of gear systems are obtained by considering the excitations induced by the number of spalling teeth, and the relative position of two faulty teeth. Moreover, to ensure the accuracy of dynamic model verification results and reduce the difficulty of fault feature analysis, a novel parameter-adaptive variational mode decomposition (VMD) method based on the ant lion optimization (ALO) is proposed to eliminate the background noise from the experimental signal. First, the ALO is used for the self-selection of the decomposition number K and the penalty factor â of the VMD. Then, the raw signal is decomposed into a set of Intrinsic Mode Functions (IMFs) by applying the ALO-VMD, and the IMFs whose effective weight kurtosis (EWK) is greater than zero are selected as the reconstructed signal. Combined with envelope spectrum analysis, the de-nosing ability of the proposed method is compared with that of the method known as particle swarm optimization-based variational mode decomposition (PSO-VMD), the fixed-parameter VMD, the empirical mode decomposition (EMD), and the local mean decomposition (LMD), respectively. The results indicate that the proposed dynamic model and background elimination method can provide a theoretical basis for spalling defect diagnosis of gear systems.
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Enefiok, Okpo, Ekom, Nkan, Imo Edwin, Odion, Joshua, and Jack, Anthony Linus. "Induction Motor Voltage Variation and Fault Adaptation in Submarines." Journal of Engineering Research and Reports 26, no. 12 (2024): 286–304. https://doi.org/10.9734/jerr/2024/v26i121358.
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Induction motors are critical components in submarine systems, powering propulsion and auxiliary machinery under challenging operational conditions. These motors, however, are susceptible to faults such as voltage disturbances and mechanical anomalies that can compromise performance and operational safety. This research investigates the fault adaptation mechanisms for induction motors in submarine scenarios by integrating wavelet decomposition for fault detection and undervoltage relays for fault adaptation and mitigation. Wavelet transform analysis is employed to detect transient faults, specifically voltage disturbances occurring between 0.3 and 0.7 seconds. The system identifies fault characteristics in real-time using the high-resolution capabilities of discrete wavelet transforms, allowing precise localization and classification of anomalies. An undervoltage relay, integrated into the system, adapts to the fault condition by tripping the motor at 0.54 seconds to prevent prolonged exposure to damaging voltage dips. The study utilizes MATLAB/Simulink to model and analyze a 7.5 kW, 400 V, 1440 RPM induction motor operating under realistic submarine conditions. For fault identifications, twelve different scenarios are examined: Three phase to ground fault, three phase fault, double line to ground fault (AB-G), double line to ground fault (AC-G), double line to ground fault (BC-G), line to line fault (A-B), line to line fault (A-C), line to line (B-B) fault, single line to ground fault (A-G), single line to ground fault (B-G), single line to ground fault (C-G), and no faults. Also, for fault adaptation using under-voltage relay, two different scenarios are simulated for reference purpose: single line to ground and three phase to ground. Simulation results demonstrate the effectiveness of the wavelet-based detection in identifying faults early and the relay's timely intervention to protect the motor. These findings highlight the viability of integrating wavelet decomposition and adaptive relay mechanisms to enhance the resilience of induction motors in submarines.
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Kaur, Jashandeep, Manilka Jayasooriya, Muhammad Naveed Iqbal, Kamran Daniel, Noman Shabbir, and Kristjan Peterson. "Fault Detection and Protection Strategy for Multi-Terminal HVDC Grids Using Wavelet Analysis." Energies 18, no. 5 (2025): 1147. https://doi.org/10.3390/en18051147.
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The growing demand for electricity, integration of renewable energy sources, and recent advances in power electronics have driven the development of HVDC systems. Multi-terminal HVDC (MTDC) grids, enabled by Voltage Source Converters (VSCs), provide increased operational flexibility, including the ability to reverse power flow and independently control both active and reactive power. However, fault propagation in DC grids occurs more rapidly, potentially leading to significant damage within milliseconds. Unlike AC systems, HVDC systems lack natural zero-crossing points, making fault isolation more complex. This paper presents the implementation of a wavelet-based protection algorithm to detect faults in a four-terminal VSC-HVDC grid, modelled in MATLAB and SIMULINK. The study considers several fault scenarios, including two internal DC pole-to-ground faults, an external DC fault in the load branch, and an external AC fault outside the protected area. The discrete wavelet transform, using Symlet decomposition, is applied to classify faults based on the wavelet entropy and sharp voltage and current signal variations. The algorithm processes the decomposition coefficients to differentiate between internal and external faults, triggering appropriate relay actions. Key factors influencing the algorithm’s performance include system complexity, fault location, and threshold settings. The suggested algorithm’s reliability and suitability are demonstrated by the real-time implementation. The results confirmed the precise fault detection, with fault currents aligning with the values in offline models. The internal faults exhibit more entropy than external faults. Results demonstrate the algorithm’s effectiveness in detecting faults rapidly and accurately. These outcomes confirm the algorithm’s suitability for a real-time environment.
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Tadeusiewicz, Michal, and Stanislaw Halgas. "A method for fault diagnosis of nonlinear circuits." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 6 (2019): 1770–81. http://dx.doi.org/10.1108/compel-03-2019-0101.
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Purpose The purpose of this paper is to develop a method for multiple soft fault diagnosis of nonlinear circuits including fault detection, identification of faulty elements and estimation of their values in real circumstances. Design/methodology/approach The method for fault diagnosis proposed here uses a measurement test leading to a system of nonlinear equations expressing the measured quantities in terms of the circuit parameters. Nonlinear functions, which appear in these equations are not given in explicit analytical form. The equations are solved using a homotopy concept. A key problem of the solvability of the equations is considered locally while tracing the solution path. Actual faults are selected on the basis of the observation that the probability of faults in fewer number of elements is greater than in a larger number of elements. Findings The results indicate that the method is an effective tool for testing nonlinear circuits including bipolar junction transistors and junction field effect transistors. Originality/value The homotopy method is generalized and associated with a restart procedure and a numerical algorithm for solving differential equations. Testable sets of elements are found using the singular value decomposition. The procedure for selecting faulty elements, based on the minimal fault number rule, is developed. The method comprises both theoretical and practical aspects of fault diagnosis.
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Bi, Xiaobo, Jiansheng Lin, Daijie Tang, et al. "VMD-KFCM Algorithm for the Fault Diagnosis of Diesel Engine Vibration Signals." Energies 13, no. 1 (2020): 228. http://dx.doi.org/10.3390/en13010228.
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Accurate and timely fault diagnosis for the diesel engine is crucial to guarantee it works safely and reliably, and reduces the maintenance costs. A novel diagnosis method based on variational mode decomposition (VMD) and kernel-based fuzzy c-means clustering (KFCM) is proposed in this paper. Firstly, the VMD algorithm is optimized to select the most suitable K value adaptively. Then KFCM is employed to classify the feature parameters of intrinsic mode functions (IMFs). Through the comparison of many different parameters, the singular value is selected finally because of the good classification effect. In this paper, the diesel engine fault simulation experiment was carried out to simulate various faults including valve clearance fault, fuel supply fault and common rail pressure fault. Each kind of machine fault varies in different degrees. To prove the effectiveness of VMD-KFCM, the proposed method is compared with empirical mode decomposition (EMD)-KFCM, ensemble empirical mode decomposition (EEMD)-KFCM, VMD-back propagation neural network (BPNN), and VMD-deep belief network (DBN). Results show that VMD-KFCM has advantages in accuracy, simplicity, and efficiency. Therefore, the method proposed in this paper can be used for diesel engine fault diagnosis, and has good application prospects.
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Zhong, Xianyou, Liu He, Gang Wan, and Yang Zhao. "Fault diagnosis for rolling bearings based on generalised dispersive mode decomposition and accugram." Insight - Non-Destructive Testing and Condition Monitoring 66, no. 2 (2024): 74–81. http://dx.doi.org/10.1784/insi.2024.66.2.74.
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Bearing fault diagnosis helps to ensure the safe operation of electromechanical equipment and reduce unnecessary losses due to downtime. The interference of noise in the signal poses a challenge in the effective identification of rolling bearing faults. To address the above problems, this paper proposes a rolling bearing fault diagnosis (RBFD) method based on generalised dispersive mode decomposition (GDMD) and an accugram. Firstly, the bearing signal is decomposed using GDMD and the optimal number of decomposition modes is chosen using a new index based on the correlation coefficient and accuracy. According to the number of determined decomposition modes, the fault signal is reconstructed. Then, the centre frequency and bandwidth of the resonant frequency are determined using an accugram. Finally, the fault signal is filtered and analysed using a square envelope spectrum to achieve rolling bearing fault diagnosis. Experimental signal analysis verifies the effectiveness and feasibility of the method. The method is applied to the early fault diagnosis of rolling bearings and compared with kurtogram and accugram results. The results show that the approach can not only effectively avoid the interference of external impacts but it can also correctly recognise the fault characteristic frequency band.
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Camarena-Martinez, David, Jose R. Huerta-Rosales, Juan P. Amezquita-Sanchez, David Granados-Lieberman, Juan C. Olivares-Galvan, and Martin Valtierra-Rodriguez. "Variational Mode Decomposition-Based Processing for Detection of Short-Circuited Turns in Transformers Using Vibration Signals and Machine Learning." Electronics 13, no. 7 (2024): 1215. http://dx.doi.org/10.3390/electronics13071215.
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Transformers are key elements in electrical systems. Although they are robust machines, different faults can appear due to their inherent operating conditions, e.g., the presence of different electrical and mechanical stresses. Among the different elements that compound a transformer, the winding is one of the most vulnerable parts, where the damage of turn-to-turn short circuits is one of the most studied faults since low-level damage (i.e., a low number of short-circuited turns—SCTs) can lead to the overall fault of the transformer; therefore, early fault detection has become a fundamental task. In this regard, this paper presents a machine learning-based method to diagnose SCTs in the transformer windings by using their vibrational response. In general, the vibration signals are firstly decomposed by means of the variational mode decomposition method, where a comparison with the empirical mode decomposition (EMD) method and the ensemble empirical mode decomposition (EEMD) method is also carried out. Then, entropy, energy, and kurtosis indices are obtained from each decomposition as fault indicators, where both the combination of features and the dimensionality reduction by using the principal component analysis (PCA) method are analyzed for the global effectiveness improvement and the computational burden reduction. Finally, a pattern recognition algorithm based on artificial neural networks (ANNs) is used for automatic fault detection. The obtained results show 100% effectiveness in detecting seven fault conditions, i.e., 0 (healthy), 5, 10, 15, 20, 25, and 30 SCTs.
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Cui, Lingli, Na Wu, Daiyi Mo, Huaqing Wang, and Peng Chen. "CQFB and PBP in Diagnosis of Local Gear Fault." Advances in Mechanical Engineering 6 (January 1, 2014): 670725. http://dx.doi.org/10.1155/2014/670725.
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The vibration signal of local gear fault is mainly composed of two components. One is the resonant signal and noise signal and the other one is the transient impulse signal including fault information. The quality factors corresponding to the two components are different. Hence, a method to diagnose local gear fault based on composite quality factor basis and parallel basis pursuit is proposed. First, two different quality factors bases are established using wavelet transform of variable quality factors to obtain the decomposition coefficient. Next, the parallel basis pursuit is adopted for the optimization of the decomposition coefficient. With the derived optimal decomposition coefficient, the resonant components with different quality factors can be reconstructed. By discussing the sparsity of signals treated with different quality factors bases, the suitable composite quality factor basis is selected to perform sparse decomposition on the signal. Besides, the obtained resonant component with low quality factor is subject to demodulation analysis, so as to derive the fault information. The feasibility and validity of the algorithm are shown by the results from simulation signal and practical application of local gear faults.
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Shayeghan, Mona, Marco Di Benedetto, Alessandro Lidozzi, and Luca Solero. "HIL-Based Fault-Tolerant Vector Space Decomposition Control for a Six-Phase PMSM Fed by a Five-Level CHB Converter." Energies 18, no. 3 (2025): 507. https://doi.org/10.3390/en18030507.
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The growing demand for higher reliability and efficiency in modern electric drives, coupled with the increasing adoption of multi-phase machines, has necessitated advancements in fault-tolerant control strategies. This paper presents a fault tolerance analysis for a six-phase permanent magnet synchronous machine (PMSM) connected to a five-level cascaded H-bridge converter, employing a level-shift pulse width modulation (LSPWM) technique. Unlike existing strategies, this work integrates a unique combination of three key innovations: first, a fault detection mechanism capable of identifying faults in both machine phases and inverter legs with high precision; second, an open-circuit fault compensation strategy that dynamically reconfigures the faulty inverter phase leg into a two-level topology to reduce losses and preserve healthy switches; and third, a modified closed-loop control method designed specifically to mitigate the adverse effects of short-circuit faults while maintaining system stability. The proposed approach is validated through rigorous simulations in Simulink and Hardware-in-the-Loop (HIL) tests, demonstrating its robustness and applicability in high-reliability applications.