Journal articles: 'Incipient fault (IF)'

1

Deng, Yong, Yibing Shi, and Wei Zhang. "Diagnosis of Incipient Faults in Nonlinear Analog Circuits." Metrology and Measurement Systems 19, no. 2 (January 1, 2012): 203–18. http://dx.doi.org/10.2478/v10178-012-0018-7.

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Diagnosis of Incipient Faults in Nonlinear Analog Circuits Considering the problem to diagnose incipient faults in nonlinear analog circuits, a novel approach based on fractional correlation is proposed and the application of the subband Volterra series is used in this paper. Firstly, the subband Volterra series is calculated from the input and output sequences of the circuit under test (CUT). Then the fractional correlation functions between the fault-free case and the incipient faulty cases of the CUT are derived. Using the feature vectors extracted from the fractional correlation functions, the hidden Markov model (HMM) is trained. Finally, the well-trained HMM is used to accomplish the incipient fault diagnosis. The simulations illustrate the proposed method and show its effectiveness in the incipient fault recognition capability.

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Mharakurwa, Edwell T., G. N. Nyakoe, and A. O. Akumu. "Power Transformer Fault Severity Estimation Based on Dissolved Gas Analysis and Energy of Fault Formation Technique." Journal of Electrical and Computer Engineering 2019 (February 3, 2019): 1–10. http://dx.doi.org/10.1155/2019/9674054.

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Decision making on transformer insulation condition based on the evaluated incipient faults and aging stresses has been the norm for many asset managers. Despite being the extensively applied methodology in power transformer incipient fault detection, solely dissolved gas analysis (DGA) techniques cannot quantify the detected fault severity. Fault severity is the core property in transformer maintenance rankings. This paper presents a fuzzy logic methodology in determining transformer faults and severity through use of energy of fault formation of the evolved gasses during transformer faulting event. Additionally, the energy of fault formation is a temperature-dependent factor for all the associated evolved gases. Instead of using the energy-weighted DGA, the calculated total energy of related incipient fault is used for severity determination. Severity of faults detected by fuzzy logic-based key gas method is evaluated through the use of collected data from several in-service and faulty transformers. DGA results of oil samples drawn from transformers of different specifications and age are used to validate the model. Model results show that correctly detecting fault type and its severity determination based on total energy released during faults can enhance decision-making in prioritizing maintenance of faulty transformers.

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Zhang, Zhao, and Xiao He. "Fault-Structure-Based Active Fault Diagnosis: A Geometric Observer Approach." Energies 13, no. 17 (August 31, 2020): 4475. http://dx.doi.org/10.3390/en13174475.

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Fault diagnosis techniques can be classified into passive and active types. Passive approaches only utilize the original input and output signals of the system. Because of the small amplitudes, the characteristics of incipient faults are not fully represented in the data of the system, so it is difficult to detect incipient faults by passive fault diagnosis techniques. In contrast, active methods can design auxiliary signals for specific faults and inject them into the system to improve fault diagnosis performance. Therefore, active fault diagnosis techniques are utilized in this article to detect and isolate incipient faults based on the fault structure. A new framework based on observer approach for active fault diagnosis is proposed and the geometric approach based fault diagnosis observer is introduced to active fault diagnosis for the first time. Based on the dynamic equations of residuals, auxiliary signals are designed to enhance the diagnosis performance for incipient faults that have specific structures. In addition, the requirements that auxiliary signals need to meet are discussed. The proposed method can realize the seamless combination of active fault diagnosis and passive fault diagnosis. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed approach, and it is indicated that the proposed method significantly improves the accuracy of the diagnosis for incipient faults.

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Zhang, Ge, Qiong Yang, Guotong Li, Jiaxing Leng, and Long Wang. "A Satellite Incipient Fault Detection Method Based on Local Optimum Projection Vector and Kullback-Leibler Divergence." Applied Sciences 11, no. 2 (January 15, 2021): 797. http://dx.doi.org/10.3390/app11020797.

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Timely and effective detection of potential incipient faults in satellites plays an important role in improving their availability and extending their service life. In this paper, the problem of detecting incipient faults using projection vector (PV) and Kullback-Leibler (KL) divergence is studied in the context of detecting incipient faults in satellites. Under the assumption that the variables obey a multidimensional Gaussian distribution and using KL divergence to detect incipient faults, this paper models the optimum PV for detecting incipient faults as an optimization problem. It proves that the PVs obtained by principal component analysis (PCA) are not necessarily the optimum PV for detecting incipient faults. It then compares the on-line probability density function (PDF) with the reference PDF for detecting incipient faults on the local optimum PV. A numerical example and a real satellite fault case were used to assess the validity and superiority of the method proposed in this paper over conventional methods. Since the method takes into account the characteristics of the actual incipient faults, it is more adaptable to various possible incipient faults. Fault detection rates of three simulated faults and the real satellite fault are 98%, 84%, 93% and 92%, respectively.

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Chen, Hongtian, Bin Jiang, and Ningyun Lu. "Data-Driven Incipient Sensor Fault Estimation with Application in Inverter of High-Speed Railway." Mathematical Problems in Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/8937356.

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Incipient faults in high-speed railway have been rarely considered before developing into faults or failures. In this paper, a new data-driven incipient fault estimate (FE) methodology is proposed under multivariate statistics frame, which incorporates with Kullback-Leibler divergence (KLD) in information domain and neural network approximation in machine learning. By defining one sensitive fault indicator (SFI), the incipient fault amplitude can be precisely estimated. According to the experimental platform of China Railway High-speed 2 (CRH2), the proposed incipient FE algorithm is examined, and the more sensitivity and accuracy to tiny abnormality are demonstrated. Followed by the incipient FE results, several factors on FE performance are further analyzed.

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Wang, Manran, Li Guo, Jinhao Chen, Bingqi Lv, and Yang Yang. "Superresolution Reconstruction of Electrical Equipment Incipient Fault." Journal of Control Science and Engineering 2018 (August 7, 2018): 1–11. http://dx.doi.org/10.1155/2018/1630402.

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With the rapid development of industry and technology, the electrical power system becomes more complex and the electrical equipment becomes more diverse. Defective equipment is often the cause of industrial accidents and electrical injuries, which can result in serious injuries, such as electrocution, burns, and electrical shocks. In some cases, electrical equipment fault may result in death. However, in some special situation, some fault is very small even invisible, such as equipment aging, holes, and cracks, so the detection of these incipient faults is difficult or even impossible. These potential incipient faults become the biggest hidden danger in the electrical equipment and electricity power system. For these reasons, this paper proposes a superresolution reconstruction method for electrical equipment incipient fault to ensure complete detection in electrical equipment, which aims to guarantee the security of electrical power system operation and industry production. Experimental results show that this method can get a state-of-the-art reconstruction effect of incipient fault, so as to provide reliable fault detection of electrical power system.

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Shi, Huaitao, Jin Guo, Zhe Yuan, Zhenpeng Liu, Maxiao Hou, and Jie Sun. "Incipient Fault Detection of Rolling Element Bearings Based on Deep EMD-PCA Algorithm." Shock and Vibration 2020 (October 26, 2020): 1–17. http://dx.doi.org/10.1155/2020/8871433.

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Due to the relatively weak early fault characteristics of rolling bearings, the difficulty of early fault detection increases. For unsolving this problem, an incipient fault detection method based on deep empirical mode decomposition and principal component analysis (Deep EMD-PCA) is proposed. In this method, multiple data processing layers are created to extract weak incipient fault features, and EMD is used to decompose the vibration signal. This method establishes an accurate data mode, which can improve the incipient fault detection capability. It overcomes the difficulties of incipient fault detection, in which weak fault features can be extracted from the background of strong noise. From a theoretical point of view, this paper proves that the Deep EMD-PCA method can retain more variance information and has a good early fault detection ability. The experiment results indicate that the detection rate of Deep EMD-PCA is about 85%, and the failure detection delay time is almost zero. The incipient faults of rolling element bearings can be detected accurately and timely by Deep EMD-PCA. The method effectively improves the accuracy and timeliness of fault detection under actual working conditions and has good practical application value.

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Hoang, Ngoc-Bach, and Hee-Jun Kang. "Incipient wheel fault identification in mobile robots using neural networks and nonlinear least squares." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 3 (August 9, 2016): 446–58. http://dx.doi.org/10.1177/0954406215616650.

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In this paper, we present a novel method for fault identification in the case of an incipient wheel fault in mobile robots. First, a three-layer neural networks is established to estimate the deviation of the robot dynamics due to the process fault. The estimate of the faulty dynamic model is based on a combination of the nominal dynamic model and the neural network output. Then, by replacing the faulty dynamic model with its estimate value, the primary estimates of the wheel radius appear as the solutions of two quadratic equations. Next, a simple and efficient way to perform these primary estimate selections is proposed in order to eliminate undesired primary estimates. A recursive nonlinear least squares is applied in order to obtain a smooth estimate of the wheel radius. Two computer simulation examples using Matlab/Simulink show that the proposed method is very effective for incipient fault identification in the setting of both left and right wheel faults.

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Xia, Jingping, Bin Jiang, Ke Zhang, and Jinfa Xu. "Robust Fault Diagnosis Design for Linear Multiagent Systems with Incipient Faults." Mathematical Problems in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/436935.

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The design of a robust fault estimation observer is studied for linear multiagent systems subject to incipient faults. By considering the fact that incipient faults are in low-frequency domain, the fault estimation of such faults is proposed for discrete-time multiagent systems based on finite-frequency technique. Moreover, using the decomposition design, an equivalent conclusion is given. Simulation results of a numerical example are presented to demonstrate the effectiveness of the proposed techniques.

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Li, Fu Cai, Jin Chen, Gui Cai Zhang, and Zheng Jia He. "Wavelet Transform Domain Filter and Its Application in Incipient Fault Prognosis." Key Engineering Materials 293-294 (September 2005): 127–34. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.127.

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Noise is the biggest obstacle that makes the incipient fault prognosis results uncorrected. According to the theories of correlation analysis and threshold de-noising by wavelets, wavelet transform domain filter (WTDF) is constructed. WTDF is an iterative process. By selecting the process parameters adaptively, WTDF can de-noise signal efficiently. More important, the faint component in the signal will become stronger. WTDF method is used to analyze the signals collected from a bearing that has incipient unbalance and misalignment faults. Results show that WTDF is effective for bearing incipient fault prognosis.

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Guo, Yu, Xing Wu, Jing Na, and Rong Fong Fung. "Incipient Faults Identification in Gearbox by Combining Kurtogram and Independent Component Analysis." Applied Mechanics and Materials 764-765 (May 2015): 309–13. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.309.

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Envelope analysis is a popular incipient fault identification tool for rolling element bearings (REBs) and gears. However, in some harsh conditions where more than one fault of REBs and gears exists simultaneously in a gearbox. In general, only the characteristic frequencies of the stronger vibration can be exposed clearly, and the others may be missed by conventional envelope analysis. To address this issue, an incipient faults detection scheme combining the kurtogram and independent component analysis (ICA) for gearbox faults diagnosis is proposed in this paper. In the proposed scheme, multi-channel vibrations are acquired from the gearbox synchronously at first. Subsequently, the vibration envelopes from each channel are extracted by the novel fast kurtogram algorithm. Then, the independent component analysis algorithm is utilized to separate the envelopes. As a result, the independent envelope components corresponding to different sources are obtained. Finally, the characteristic frequencies of the incipient faults of rolling element bearings and/or gears in a gearbox can be clearly exposed in envelope spectral plots. An experiment on a gearbox test rig which has both a REB fault and a gear fault is conducted to compare the conventional envelope analysis scheme and the proposed scheme. Test results show that the proposed scheme is more effective to identify the incipient faults of REBs and gears simultaneously existing in a gearbox.

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Jie, Li, and Zhao Jianmin. "Incipient Gearbox Fault Diagnosis Based on the Reverse State Transformation of the Chaotic Duffing Oscillator and Sampling Integral Technology." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/535398.

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Incipient fault for a gearbox diagnosis is difficult because the signals with low signal-to-noise ratio (SNR) are corrupted with background noise. A method based on chaos theory and sampling integral technology will be presented to detect the incipient fault of gearbox according to the characters of the gearbox vibration signals. Sampling integral technology was used to improve the tracking ability of fault signals with lower SNR. The small changes in the sidebands of meshing frequency can be detected by the transformation of chaotic phase diagram and its Hu moment invariants, and on this basis the incipient faults can be diagnosed. The results based on gearboxes experiment justify the effectiveness of the method.

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Fair, Martene, and Stephen L. Campbell. "Active incipient fault detection with two simultaneous faults." IFAC Proceedings Volumes 42, no. 8 (2009): 573–78. http://dx.doi.org/10.3182/20090630-4-es-2003.00095.

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Gu, Quan, Chang Zheng Chen, Xiang Jun Kong, Xian Ming Sun, Bo Zhou, and Yan Ling Gu. "Fault Diagnosis of the Wind Turbine Main Bearing through Multifractal Theory." Advanced Materials Research 644 (January 2013): 337–40. http://dx.doi.org/10.4028/www.scientific.net/amr.644.337.

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Because the vibration signals of faulty wind turbine are non-linear and non-stationary, to obtain the obvious fault features become difficult. In this study, the incipient fault of the main bearing used in large scale wind turbine is studied by using a multifractal method based on the Wavelet Modulus Maxima (WTMM) method. The real vibration signals from the main bearings are analyzed using the multifractal spectrum. The spectrum of the vibration signals is quantified by spectral characteristics including its range and the Hölder exponent corresponding to the maximum dimension. The results show that the range of Hölder exponent of the main bearing which worked normally is much narrower. While the ranges of the vibration signals of the main bearing with incipient fault are wider. We also found that the fault features are different at various wind turbine rotational frequencies. Those demonstrate that the incipient fault features of main bearing of large scale wind turbine can be extract effectively using the multifractal spectrum obtained from WTMM method.

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Wang, Heng-di, Si-er Deng, Jian-xi Yang, Hui Liao, and Wen-bo Li. "Parameter-Adaptive VMD Method Based on BAS Optimization Algorithm for Incipient Bearing Fault Diagnosis." Mathematical Problems in Engineering 2020 (February 25, 2020): 1–15. http://dx.doi.org/10.1155/2020/5659618.

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In view of the incipient fault characteristics are difficult to be extracted from the raw bearing fault signals, an incipient bearing fault diagnosis method based on parameter-adaptive variational mode decomposition (VMD) is proposed. The beetle antennae search (BAS) algorithm is adopted to seek for the optimal combination of the VMD parameters. The reciprocals of the calculated kurtosis values of intrinsic mode functions (IMFs) decomposed via VMD are employed as a fitness function in the searching process. The optimal mode number and the quadratic penalty term of VMD are adaptively set after the search. Afterwards, a vibration signal is decomposed into a set of IMFs using the parameter-adaptive VMD, and the IMF with the maximal kurtosis value is selected as the sensitive one. The selected IMF is further analyzed by Hilbert envelope demodulation. The resulting envelope spectrum can show the significant fault impulse characteristics which are highly helpful to diagnose incipient bearing faults. The kurtosis and the proportion of fault energy are introduced as the input vector of the extreme learning machine (ELM). Comparisons have been conducted via ELM to evaluate the performance by using EMD and the fixed-parameter VMD. The experimental results demonstrate that the proposed method is more effective in extracting the incipient bearing fault characteristics.

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Liu, Ning, Bo Fan, Xianyong Xiao, and Xiaomei Yang. "Cable Incipient Fault Identification with a Sparse Autoencoder and a Deep Belief Network." Energies 12, no. 18 (September 5, 2019): 3424. http://dx.doi.org/10.3390/en12183424.

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Incipient faults in power cables are a serious threat to power safety and are difficult to accurately identify. The traditional pattern recognition method based on feature extraction and feature selection has strong subjectivity. If the key feature information cannot be extracted accurately, the recognition accuracy will directly decrease. To accurately identify incipient faults in power cables, this paper combines a sparse autoencoder and a deep belief network to form a deep neural network, which relies on the powerful learning ability of the neural network to classify and identify various cable fault signals, without requiring preprocessing operations for the fault signals. The experimental results demonstrate that the proposed approach can effectively identify cable incipient faults from other disturbances with a similar overcurrent phenomenon and has a higher recognition accuracy and reliability than the traditional pattern recognition method.

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Ren, L., Z. Y. Xu, and X. Q. Yan. "Single-Sensor Incipient Fault Detection." IEEE Sensors Journal 11, no. 9 (September 2011): 2102–7. http://dx.doi.org/10.1109/jsen.2010.2093879.

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Baghli, Delpha, Diallo, Hallouche, Mba, and Wang. "Three-Level NPC Inverter Incipient Fault Detection and Classification using Output Current Statistical Analysis." Energies 12, no. 7 (April 9, 2019): 1372. http://dx.doi.org/10.3390/en12071372.

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This paper deals with open switch Fault Detection and Diagnosis (FDD) in three-level Neutral Point Clamped (NPC) inverter for electrical drives. The approach is based on the already available phase current time series measurements for different operating conditions (motor speed, load, and environment noise). Both fault detection and classification are studied and the efficiency performances of the proposed selected features are shown. For the fault detection, we focus on the first four statistical moments and the extracted features and then the Cumulative Sum (CUSUM) algorithm as the feature analysis technique to improve the performances. For the classification study, we propose to couple the knowledge on the faulty system brought by the statistical moments and the Kullback-Leibler divergence particularly suitable for the detection of incipient changes. The Principal Component Analysis (PCA) is then used to perform the classification. A 2D framework is obtained, which allows the faults to be classified efficiently within the considered operating conditions for all the selected fault durations.

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Shi, Huaitao, Jin Guo, Xiaotian Bai, Lei Guo, Zhenpeng Liu, and Jie Sun. "Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings." Applied Sciences 10, no. 7 (April 3, 2020): 2443. http://dx.doi.org/10.3390/app10072443.

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The incipient fault detection technology of rolling bearings is the key to ensure its normal operation and is of great significance for most industrial processes. However, the vibration signals of rolling bearings are a set of time series with non-linear and timing correlation, and weak incipient fault characteristics of rolling bearings bring about obstructions for the fault detection. This paper proposes a nonlinear dynamic incipient fault detection method for rolling bearings to solve these problems. The kernel function and the moving window algorithm are used to establish a non-linear dynamic model, and the real-time characteristics of the system are obtained. At the same time, the deep decomposition method is used to extract weak fault characteristics under the strong noise, and the incipient failures of rolling bearings are detected. Finally, the validity and feasibility of the scheme are verified by two simulation experiments. Experimental results show that the fault detection rate based on the proposed method is higher than 85% for incipient fault of rolling bearings, and the detection delay is almost zero. Compared with the detection performance of traditional methods, the proposed nonlinear dynamic incipient fault detection method is of better accuracy and applicability.

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Zhang, Ge, Qiong Yang, Guotong Li, Jiaxing Leng, and Mubiao Yan. "A Satellite Incipient Fault Detection Method Based on Decomposed Kullback–Leibler Divergence." Entropy 23, no. 9 (September 9, 2021): 1194. http://dx.doi.org/10.3390/e23091194.

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Detection of faults at the incipient stage is critical to improving the availability and continuity of satellite services. The application of a local optimum projection vector and the Kullback–Leibler (KL) divergence can improve the detection rate of incipient faults. However, this suffers from the problem of high time complexity. We propose decomposing the KL divergence in the original optimization model and applying the property of the generalized Rayleigh quotient to reduce time complexity. Additionally, we establish two distribution models for subfunctions F1(w) and F3(w) to detect the slight anomalous behavior of the mean and covariance. The effectiveness of the proposed method was verified through a numerical simulation case and a real satellite fault case. The results demonstrate the advantages of low computational complexity and high sensitivity to incipient faults.

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Swana, Elsie, and Wesley Doorsamy. "An Unsupervised Learning Approach to Condition Assessment on a Wound-Rotor Induction Generator." Energies 14, no. 3 (January 25, 2021): 602. http://dx.doi.org/10.3390/en14030602.

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Accurate online diagnosis of incipient faults and condition assessment on generators is especially challenging to automate through supervised learning techniques, because of data imbalance. Fault-condition training and test data are either not available or are experimentally emulated, and therefore do not precisely account for all the eventualities and nuances of practical operating conditions. Thus, it would be more convenient to harness the ability of unsupervised learning in these applications. An investigation into the use of unsupervised learning as a means of recognizing incipient fault patterns and assessing the condition of a wound-rotor induction generator is presented. High-dimension clustering is performed using stator and rotor current and voltage signatures measured under healthy and varying fault conditions on an experimental wound-rotor induction generator. An analysis and validation of the clustering results are carried out to determine the performance and suitability of the technique. Results indicate that the presented technique can accurately distinguish the different incipient faults investigated in an unsupervised manner. This research will contribute to the ongoing development of unsupervised learning frameworks in data-driven diagnostic systems for WRIGs and similar electrical machines.

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Huang, Haifeng, Huajiang Ouyang, Hongli Gao, Liang Guo, Dan Li, and Juan Wen. "A Feature Extraction Method for Vibration Signal of Bearing Incipient Degradation." Measurement Science Review 16, no. 3 (June 1, 2016): 149–59. http://dx.doi.org/10.1515/msr-2016-0018.

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Abstract Detection of incipient degradation demands extracting sensitive features accurately when signal-to-noise ratio (SNR) is very poor, which appears in most industrial environments. Vibration signals of rolling bearings are widely used for bearing fault diagnosis. In this paper, we propose a feature extraction method that combines Blind Source Separation (BSS) and Spectral Kurtosis (SK) to separate independent noise sources. Normal, and incipient fault signals from vibration tests of rolling bearings are processed. We studied 16 groups of vibration signals (which all display an increase in kurtosis) of incipient degradation after they are processed by a BSS filter. Compared with conventional kurtosis, theoretical studies of SK trends show that the SK levels vary with frequencies and some experimental studies show that SK trends of measured vibration signals of bearings vary with the amount and level of impulses in both vibration and noise signals due to bearing faults. It is found that the peak values of SK increase when vibration signals of incipient faults are processed by a BSS filter. This pre-processing by a BSS filter makes SK more sensitive to impulses caused by performance degradation of bearings.

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Patton, R. J., J. Chen, and G. P. Liu. "Robust fault detection of dynamic systems via genetic algorithms." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 211, no. 5 (August 1, 1997): 357–64. http://dx.doi.org/10.1243/0959651971539885.

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This paper presents a new approach to the design of robust fault detection systems via a genetic algorithm. To achieve robustness, a number of performance indices are introduced, which are expressed in the frequency domain to account for the frequency distributions of incipient faults, noise and modelling uncertainty. All objectives are then reformulated into a set of inequality constraints on performance indices. A genetic algorithm is thus used to search an optimal solution to satisfy these inequality constraints. The approach developed is applied to a flight control system example and results show that incipient sensor faults can be detected reliably in the presence of modelling uncertainty.

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Wang, Heng-di, Si-er Deng, Jian-xi Yang, and Hui Liao. "A fault diagnosis method for rolling element bearing (REB) based on reducing REB foundation vibration and noise-assisted vibration signal analysis." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 7 (August 6, 2018): 2574–87. http://dx.doi.org/10.1177/0954406218791209.

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Owing to the problem of the incipient fault characteristics being difficult to be extracted from the raw vibration signal of rolling element bearing, based on the empirical mode decomposition and kurtosis criteria, a fault diagnosis method for rolling element bearing is proposed by reducing rolling element bearing foundation vibration and noise-assisted vibration signal analysis. Firstly, rolling element bearing vibration signal is decomposed into a set of intrinsic mode functions using empirical mode decomposition and the intrinsic mode function component with the maximal kurtosis value is selected. Afterwards, zero mean normalization is applied to the selected intrinsic mode function component, and then the intrinsic mode function’s foundation vibration components within [Formula: see text] are removed to minimize the interference. In order to eliminate interruption and intermittency after removal of the foundation vibration components, white noise is added to the newly generated signal. The noise-added signal is decomposed via empirical mode decomposition, and later on, IMF1 with the highest frequency band is selected and demodulated using envelope analysis. The resulting envelope spectrum can show more significant fault pulse characteristics, which are highly helpful to diagnose the rolling element bearing incipient faults. The proposed method in this paper was applied to the fault diagnosis for low noise REB 6203 and the testing results showed that the method could identify the rolling element bearing incipient faults accurately and quickly.

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Xu, Li Jia, and Zhi Liang Kang. "Study on Fault Diagnosis in Analog Circuit." Advanced Materials Research 490-495 (March 2012): 628–32. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.628.

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Hidden Markov model (HMM) for the diagnosis of incipient faults in analog circuits is presented. Firstly, output voltage signals under faulty conditions are obtained with simulation. Subsequently, output voltages corresponding to the test frequencies are extracted from the response of analog circuits. Finally, the output voltage vectors are used to form the observation sequences, which are sent to training HMM to accomplish the diagnosis of the incipient faults. The performance of the proposed method is tested, and it indicates that the method is effective and has better recognition capability than the popularly used back-propagation (BP) network.

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Li, Xiaochuan, David Mba, Demba Diallo, and Claude Delpha. "Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults." Energies 12, no. 4 (February 22, 2019): 726. http://dx.doi.org/10.3390/en12040726.

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This study puts forward a novel diagnostic approach based on canonical variate residuals (CVR) to implement incipient fault diagnosis for dynamic process monitoring. The conventional canonical variate analysis (CVA) fault detection approach is extended to form a new monitoring index based on Hotelling’s T 2 , Q and a CVR-based monitoring index, T d . A CVR-based contribution plot approach is also proposed based on Q and T d statistics. Two performance metrics: (1) false alarm rate and (2) missed detection rate are used to assess the effectiveness of the proposed approach. The CVR diagnostic approach was validated on incipient faults in a continuous stirred tank reactor (CSTR) system and an operational centrifugal compressor.

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Ling, Jeeng Min, Ming Jong Lin, and Chao Tang Yu. "Transformer Fault Diagnosis with the Duval Triangle and Heuristic Techniques." Applied Mechanics and Materials 535 (February 2014): 157–61. http://dx.doi.org/10.4028/www.scientific.net/amm.535.157.

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Dissolved gas analysis (DGA) is an effective tool for detecting incipient faults in power transformers. The ANSI/IEEE C57.104 standards, the most popular guides for the interpretation of gases generated in oil-immersed transformers, and the IEC-Duval triangle method are integrated to develop the proposed power transformer fault diagnosis method. The key dissolved gases, including H2, CH4, C2H2, C2H4, C2H6, and total combustible gases (TCG), suggested by ASTM D3612s instruction for DGA is investigated. The tested data of the transformer oil were taken from the substations of Taiwan Power Company. Diagnosis results with the text form called IEC-Duval triangle method show the validation and accuracy to detect the incipient fault in the power transformer.

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Xin, Tingyu, Clive Roberts, Paul Weston, and Edward Stewart. "Condition monitoring of railway pantographs to achieve fault detection and fault diagnosis." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 234, no. 3 (September 19, 2018): 289–300. http://dx.doi.org/10.1177/0954409718800567.

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Railway pantographs are used around the world for collecting electrical energy to power railway vehicles from the overhead catenary. Faults in the pantograph system degrade the quality of the contact between the pantograph and catenary and reduce the reliability of railway operations. To maintain the pantographs in a good working condition, regular inspection tasks are carried out at rolling stock depots. The current pantograph inspections, in general, are only effective for the detection of major faults, providing limited incipient fault detection or fault diagnosis capabilities. Condition monitoring of pantographs has the potential to improve pantograph performance and reduce maintenance costs. As a first step in the realisation of practical pantograph condition monitoring, a laboratory-based pantograph test rig has been developed to gain an understanding of pantograph dynamic behaviours, particularly when incipient faults are present. In the first work of this kind, dynamic response data have been acquired from a number of pantographs that have allowed fault detection and diagnosis algorithms to be developed and verified. Three tests have been developed: (i) a hysteresis test that uses different excitation speeds, (ii) a frequency response test that uses different excitation frequencies, and (iii) a novel changing gradient test. Verification tests indicate that the hysteresis test is effective in detecting and diagnosing pneumatic actuator and elbow joint faults. The frequency response test is able to monitor the overall degradation in the pantograph. The changing gradient test provides fault detection and diagnosis in the pantograph head suspension and pneumatic actuator. The test rig and fault detection and diagnosis algorithms are now being developed into a depot-based prototype together with a number of industrial partners.

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YAQUB, Muhammad F., Iqbal GONDAL, and Joarder KAMRUZZAMAN. "3242 Optimally Parameterized Wavelet Packet Transform for Incipient Machine Fault Diagnosis." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2011.6 (2011): _3242–1_—_3242–6_. http://dx.doi.org/10.1299/jsmelem.2011.6._3242-1_.

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He, Jing, and Changfan Zhang. "A Design Method for Fault Reconfiguration and Fault-Tolerant Control of a Servo Motor." Mathematical Problems in Engineering 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/647571.

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A design scheme that integrates fault reconfiguration and fault-tolerant position control is proposed for a nonlinear servo system with friction. Analysis of the non-linear friction torque and fault in the system is used to guide design of a sliding mode position controller. A sliding mode observer is designed to achieve fault reconfiguration based on the equivalence principle. Thus, active fault-tolerant position control of the system can be realized. A real-time simulation experiment is performed on a hardware-in-loop simulation platform. The results show that the system reconfigures well for both incipient and abrupt faults. Under the fault-tolerant control mechanism, the output signal for the system position can rapidly track given values without being influenced by faults.

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31

Ge, Wenshuang, Jing Wang, Jinglin Zhou, Haiyan Wu, and Qibing Jin. "Incipient Fault Detection Based on Fault Extraction and Residual Evaluation." Industrial & Engineering Chemistry Research 54, no. 14 (April 3, 2015): 3664–77. http://dx.doi.org/10.1021/acs.iecr.5b00567.

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Bouakoura, Mohamed, Mohamed-Said Naït-Saïd, and Nasreddine Nait-Said. "Incipient Inter-Turn Short Circuit Fault Estimation Based on a Faulty Model Observer and ANN-Method for Induction Motor Drives." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 12, no. 4 (August 23, 2019): 374–83. http://dx.doi.org/10.2174/2352096511666180705113021.

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Background: According to statistics, short circuit faults are the second most frequent faults in induction motors. Thus, in this paper, we investigated inter turn short circuit faults in their early stage. Methods: A new equivalent model of the induction motor with turn to turn fault on one phase has been developed. This model has been used to establish two schemes to estimate the severity of the short circuit fault. In the first scheme, the faulty model is considered as an observer, where a correction of an error between the measured and the estimated currents is the kernel of the fault severity estimator. However, to develop the second method, the model was required only in the training process of an artificial neural network (ANN). Since stator faults have a signature on symmetrical components of phase currents, the magnitudes and angles of these components were used with the mean speed value as inputs of the ANN. A simulation on MATLAB of both techniques has been performed with various stator frequencies. Results: The suggested schemes prove a unique efficiency in the estimation of incipient turn to turn fault. Besides, the ANN based scheme is less complex which reduces its implementation cost. Conclusion: To monitor the stator of an induction motor, the choice of the appropriate algorithm should be done according to the system in which the motor will be installed. If the motor is directing connected to the grid or fed via an inverter with a variable DC bus voltage, the observer would be better, otherwise, the ANN algorithm is recommended.

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Wang, Jing, Jingjing Zhang, Bo Qu, Haiyan Wu, and Jinglin Zhou. "Unified Architecture of Active Fault Detection and Partial Active Fault-Tolerant Control for Incipient Faults." IEEE Transactions on Systems, Man, and Cybernetics: Systems 47, no. 7 (July 2017): 1688–700. http://dx.doi.org/10.1109/tsmc.2017.2667683.

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Chen, Wen, and Fahmida N. Chowdhury. "DETECTION OF INCIPIENT FAULTS IN POST-FAULT SYSTEMS SUBJECT TO ADAPTIVE FAULT-TOLERANT CONTROL 1." IFAC Proceedings Volumes 39, no. 13 (2006): 1097–102. http://dx.doi.org/10.3182/20060829-4-cn-2909.00183.

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Kulkarni, Saurabh, Surya Santoso, and Thomas A. Short. "Incipient Fault Location Algorithm for Underground Cables." IEEE Transactions on Smart Grid 5, no. 3 (May 2014): 1165–74. http://dx.doi.org/10.1109/tsg.2014.2303483.

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36

Barater, Davide, Jesus Arellano-Padilla, and Chris Gerada. "Incipient Fault Diagnosis in Ultrareliable Electrical Machines." IEEE Transactions on Industry Applications 53, no. 3 (May 2017): 2906–14. http://dx.doi.org/10.1109/tia.2017.2660465.

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37

Madden, M. G. M., and P. J. Nolan. "Monitoring and diagnosis of multiple incipient faults using fault tree induction." IEE Proceedings - Control Theory and Applications 146, no. 2 (March 1, 1999): 204–12. http://dx.doi.org/10.1049/ip-cta:19990088.

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Wang, Ping, De Xiang Zhang, and Yan Li Liu. "Gearbox Fault Diagnosis Based on EMD and Coefficient-Energy Value." Advanced Materials Research 542-543 (June 2012): 234–37. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.234.

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This paper applies the empirical mode decomposition (EMD) methods to gearbox vibration signal analysis capture from vibrating acceleration sensor for gearbox fault diagnosis. The original modulation fault vibration signals are firstly decomposed into a number of intrinsic mode function (IMF) by the EMD method. Then the fault information diagnosis of the gearbox vibration signals can be extracted from the coefficient-energy value of intrinsic mode function. Experiment result has shown the feasibility and efficiency of the EMD algorithms and energy characteristic method in fault diagnosis and fault message abstraction. It is significant for the monitor operating state of gearbox and detects incipient faults as soon as possible.

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Venkata Ramana, D., and S. Baskar. "Incipient Fault Detection of the Inverter Fed Induction Motor Drive." International Journal of Power Electronics and Drive Systems (IJPEDS) 8, no. 2 (June 1, 2017): 722. http://dx.doi.org/10.11591/ijpeds.v8.i2.pp722-729.

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Inverter fed Induction motor drives are deployed across a variety of industrial and commercial applications. Although the drives in the question are well known for their reliable operation in any type of environment, it becomes an important daunting critical task to have them in continuous operation as per the applications’ requirement. Identifying the faulty behavior of power electronic circuits which could lead to catastrophic failures is an attractive proposition. The cost associated with building systems devoted for monitoring and diagnosis is high, however such cost could be justified for the safety-critical systems. Commonly practiced methods for improving the reliability of the power electronic systems are: designing the power circuit conservatively or having parallel redundant operation of components or circuits and clearly these two methods are expensive. An alternative to redundancy is fault tolerant control, which involves drive control algorithm, that in the event of fault occurrence, allows the drive to run in a degraded mode. Such algorithms involve on-line processing of the signals and this requires Digital Signal Processing of the signals. This paper presents the FFT and Wavelet transform techniques for on-line monitoring and analyzing the signals such as stator currents.

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Cole, M. O. T., P. S. Keogh, and C. R. Burrows. "Fault-tolerant control of rotor/magnetic bearing systems using reconfigurable control with built-in fault detection." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 214, no. 12 (December 1, 2000): 1445–65. http://dx.doi.org/10.1243/0954406001523416.

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Magnetic bearings now exist in a variety of industrial applications. However, there are still concerns over the control integrity of rotor/magnetic bearing systems and the ability of control systems to cope with possible faults that can occur during operation. Unless control systems can be developed that have the ability to maintain safe operation when the system is in a degraded or faulty state, then many, otherwise viable, magnetic bearing applications will remain unfulfilled. In this paper, a method is proposed for the design of a fault-tolerant control system that can detect and identify both incipient and sudden faults as and when they occur. A multivariable H∞ controller is reconfigured on occurrence of a fault so that stability and performance is maintained. A neural network is trained to identify faults associated with the system position transducer measurements so that the output from the neural network can be used as the decision tool for reconfiguring control. In this way, satisfactory control of the system can be maintained during failure of a control input. The method requires no knowledge of the system dynamics or system disturbances, and the network can be trained on-line. The validity of this method is demonstrated experimentally for various modes of sensor failure.

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Li, Longlong, Yahui Cui, Runlin Chen, and Xiaolin Liu. "Optimal SES Selection Based on SVD and Its Application to Incipient Bearing Fault Diagnosis." Shock and Vibration 2018 (December 2, 2018): 1–13. http://dx.doi.org/10.1155/2018/8067416.

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Rotating machinery has extensive industrial applications, and rolling element bearing (REB) is one of the core parts. To distinguish the incipient fault of bearing before it steps into serious failure is the main task of condition monitoring and fault diagnosis technology which could guarantee the reliability and security of rotating machinery. The early defect occurring in the REB is too weak and manifests itself in heavy surrounding noise, thus leading to the inefficiency of the fault detection techniques. Aiming at the vibration signal purification and promoting the potential of defects detection, a new method is proposed in this paper based on the combination of singular value decomposition (SVD) technique and squared envelope spectrum (SES). The kurtosis of SES (KSES) is employed to select the optimal singular component (SC) obtained by applying SVD to vibration signal, which provides the information of the REB for fault diagnosis. Moreover, the rolling bearing accelerated life test with the bearing running from normal state to failure is adopted to evaluate the performance of the SVD-KSES, and results demonstrate the proposed approach can detect the incipient faults from vibration signal in the natural degradation process.

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Kumar, A., and Vidya H. A. "Transformer Incipient fault prediction using Support Vector Machine (SVM)." Journal of University of Shanghai for Science and Technology 23, no. 05 (May 28, 2021): 737–44. http://dx.doi.org/10.51201/jusst/21/05208.

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The power transformer is an important link in the power system. Utilities will face a huge loss if a fault occurs transformer. The outage can cause loss to the industry sector. Transformer incipient fault can be predicted using Dissolved Gas Analysis (DGA) based on gas ratios. The current work is an effort to use SVM to predict transformer incipient fault more precisely. DGA data of various transformer oil samples were collected and analyzed to select the best SVM kernel function and kernel factor to be used and to observe the prediction accuracy.

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Chen, Wen, and Fahmida N. Chowdhury. "Analysis and detection of incipient faults in post-fault systems subject to adaptive fault-tolerant control." International Journal of Adaptive Control and Signal Processing 22, no. 9 (November 2008): 815–32. http://dx.doi.org/10.1002/acs.1021.

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Angeli, Chrissanthi, and Avraam Chatzinikolaou. "On-Line Fault Detection and Compensation of Hydraulic Driven Machines Using Modelling Techniques." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 2 (March 20, 2008): 111–15. http://dx.doi.org/10.20965/jaciii.2008.p0111.

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The development of on-line fault detection methods for drive and control systems is of main importance for the modern production technology. Modelling information improves the reliability of the diagnostic method when it is involved in a fault detection system. In this paper, the use of modelling information for the fault detection of hydraulic driven machines as well as for the compensation of incipient faults is presented. For this purpose a suitable implementation environment was developed that allows the on line interaction of real time data and simulation results and makes possible their direct effect to the actual system.

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45

Zhang, Chaolong, Yigang He, Lei Zuo, Jinping Wang, and Wei He. "A Novel Approach To Diagnosis Of Analog Circuit Incipient Faults Based On KECA And OAO LSSVM." Metrology and Measurement Systems 22, no. 2 (June 1, 2015): 251–62. http://dx.doi.org/10.1515/mms-2015-0025.

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Abstract Correct incipient identification of an analog circuit fault is conducive to the health of the analog circuit, yet very difficult. In this paper, a novel approach to analog circuit incipient fault identification is presented. Time responses are acquired by sampling outputs of the circuits under test, and then the responses are decomposed by the wavelet transform in order to generate energy features. Afterwards, lower-dimensional features are produced through the kernel entropy component analysis as samples for training and testing a one-against-one least squares support vector machine. Simulations of the incipient fault diagnosis for a Sallen-Key band-pass filter and a two-stage four-op-amp bi-quad low-pass filter demonstrate the diagnosing procedure of the proposed approach, and also reveal that the proposed approach has higher diagnosis accuracy than the referenced methods.

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Pietrzak, Przemyslaw, and Marcin Wolkiewicz. "Comparison of Selected Methods for the Stator Winding Condition Monitoring of a PMSM Using the Stator Phase Currents." Energies 14, no. 6 (March 15, 2021): 1630. http://dx.doi.org/10.3390/en14061630.

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Stator winding faults are one of the most common faults of permanent magnet synchronous motors (PMSMs), and searching for methods to efficiently detect this type of fault and at an early stage of damage is still an ongoing, important topic. This paper deals with the selected methods for detecting stator winding faults (short-circuits) of a permanent magnet synchronous motor, which are based on the analysis of the stator phase current signal. These methods were experimentally verified and their effectiveness was carefully compared. The article presents the results of experimental studies obtained from the spectral analysis of the stator phase current, stator phase current envelope, and the discrete wavelet transform. The original fault indicators (FIs) based on the observation of the symptoms of stator winding fault were distinguished using the aforementioned methods, which clearly show which symptom is most sensitive to the incipient fault of the stator winding of PMSMs.

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Wang, Xiaohui, Yanjiang Wang, Xiaogang Deng, and Zheng Zhang. "Deep Convolutional Feature-Based Probabilistic SVDD Method for Monitoring Incipient Faults of Batch Process." Energies 14, no. 11 (June 6, 2021): 3334. http://dx.doi.org/10.3390/en14113334.

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Support vector data description (SVDD) has been widely applied to batch process fault detection. However, it often performs poorly, especially when incipient faults occur, because it only considers the shallow data feature and omits the probabilistic information of features. In order to provide better monitoring performance on incipient faults in batch processes, an improved SVDD method, called deep probabilistic SVDD (DPSVDD), is proposed in this work by integrating the convolutional autoencoder and the probability-related monitoring indices. For mining the hidden data features effectively, a deep convolutional features extraction network is designed by a convolutional autoencoder, where the encoder outputs and the reconstruction errors are used as the monitor features. Furthermore, the probability distribution changes of these features are evaluated by the Kullback-Leibler (KL) divergence so that the probability-related monitoring indices are developed for indicating the process status. The applications to the benchmark penicillin fermentation process demonstrate that the proposed method has a better monitoring performance on the incipient faults in comparison to the traditional SVDD methods.

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L. Fair, Martene, and Stephen L. Campbell. "Active incipient fault detection in continuous time systems with multiple simultaneous faults." Numerical Algebra, Control & Optimization 1, no. 2 (2011): 211–24. http://dx.doi.org/10.3934/naco.2011.1.211.

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Hong, Sang-Jeen, Jae-Hyun Park, and Seung-Soo Han. "Incipient Fault Detection of Reactive Ion Etching Process." Transactions on Electrical and Electronic Materials 6, no. 6 (December 1, 2005): 262–71. http://dx.doi.org/10.4313/teem.2005.6.6.262.

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Freyermuth, B. "Knowledge based Incipient Fault Diagnosis of Industrial Robots." IFAC Proceedings Volumes 24, no. 6 (September 1991): 369–75. http://dx.doi.org/10.1016/s1474-6670(17)51169-6.

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Journal articles on the topic 'Incipient fault (IF)'

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