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Journal articles on the topic 'Fault decomposition'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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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 eff

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2

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 eig

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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 deco

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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

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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 fau

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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 propaga

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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

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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 throu

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9

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 in

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10

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 t

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11

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 fau

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12

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. Accor

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13

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 enh

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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 resid

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15

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 resid

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16

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 algo

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17

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 ba

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18

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 vecto

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19

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 a

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20

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

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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. Ultimate

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22

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 (

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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 l

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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

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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

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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 an

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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 fau

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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.

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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 (L

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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.

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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 b

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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

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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 localiza

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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

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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 m

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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 subje

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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

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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 b

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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 decomposi

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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

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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

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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 valu

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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 spalli

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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, spec

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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

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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

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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 classificatio

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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 accu

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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 t

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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 th

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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 faul

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