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1
Gupta, Abhishek, and Ramesh Kumar Pachar. "A Hybrid Signal Processing Technique for Identification and Categorization of Faults in IEEE-9 Bus System." Advanced Engineering Forum 49 (May 31, 2023): 43–55. http://dx.doi.org/10.4028/p-jkw3p9.
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A hybrid signal processing technique (HSPT) is proposed in this manuscript for identification and categorization of faults in electrical transmission network. A fault indicator (FI) is suggested by decomposition of the currents by application of Alienation coefficient (ACF), Stockwell transform (ST) and Hilbert transform (HT) for identification of faults. An indicator for ground involvement during faulty condition (SGFI) is being suggested to detect the type of fault. The categorization of faults is done by utilizing faulty phase numbers and SGFI. It is found that the proposed technique is effective in identification of faults and to classify them in different scenarios together with fault on A-phase to ground (AGF), double phase fault (ABF), fault on two phases and ground (ABGF), three phase fault (ABCF) and three phase fault including ground (ABCGF). Study is done and validated on IEEE-9 bus system using MATLAB/Simulink environment. The effectiveness and applicability of the proposed technique with respect to different parameters of faults such as Fault Incidence Angle, Fault Impedance, Line loading, Generator Supply and Noise level is also checked. The results shows that proposed scheme is able to detect and classify the faults in different faulty events.
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 (June 20, 2022): 214. http://dx.doi.org/10.18311/jmmf/2022/30060.
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Industrial machinery often breakdowns due to faults in rolling bearing. Bearing diagnosis plays a vital role in condition monitoring of machinery. Operating conditions and working environment of bearings make them prone to single or multiple faults. In this research, signals from both healthy and faulty bearings are extracted and decomposed into empirical modes. By analyzing different empirical modes from 8 derived empirical modes for healthy and faulty bearings under different fault sizes, the first mode has the most information to classify bearing condition. From the first empirical mode eight features in time domain were calculated for various bearing conditions like healthy, rolling element fault, outer and inner race fault. The feature extraction of vibration signal based on Empirical Mode Decomposition (EMD) is extensively explored and applied in diagnosis of fault in rolling bearings. This paper presents mathematical analysis for selection of valid Intrinsic Mode Functions (IMFs) of EMD. These chosen features are trained and classified using different classifiers. Among them K-star classifier is most reliable to categorize the bearing defects.
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Fang, Liang, and Hongchun Sun. "Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery." Applied Sciences 8, no. 9 (August 23, 2018): 1441. http://dx.doi.org/10.3390/app8091441.
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A method is proposed to improve the feature extraction of vibration signals of rotating machinery. Firstly, the single-channel vibration signal is decomposed with ensemble empirical mode decomposition (EEMD). Then, the number of fault signals can be estimated with singular-value decomposition (SVD). Finally, the fault signals can be extracted with kernel-independent component analysis (KICA). The advantage of this method is that it can estimate the number of fault signals of single-channel vibration signals and can extract the fault features clearly. Compared with wavelets, empirical mode decomposition (EMD), variational mode decomposition (VMD) and EEMD, the better performance of this method is proven with three experimental analyses of faulty gear, a faulty rolling bearing and a faulty shaft. The results demonstrate that the proposed method is efficient to extract the fault features of single-channel vibration signals of rotating machinery.
4
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 (November 26, 2016): 4670–83. http://dx.doi.org/10.1177/0954406216671542.
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The fault diagnosis of rotating machinery is quite important for the security and reliability of the overall mechanical equipment. As the main components in rotating machinery, the gear and the bearing are the most vulnerable to faults. In actual working conditions, there are two common types of faults in rotating machinery: the single fault and the compound fault. However, both of them are difficult to detect in the incipient stage because the weak fault characteristic signals are usually submerged by strong background noise, thus increasing the difficulty of the weak fault feature extraction. In this paper, a novel decomposition method, optimal resonance-based signal spares decomposition, is applied for the detection of those two types of faults in the rotating machinery. This method is based on the resonance-based signal spares decomposition, which can nonlinearly decompose vibration signals of rotating machinery into the high and the low resonance components. To extract the weak fault characteristic signals in the presence of strong noise effectively, the genetic algorithm is used to obtain the optimal decomposition parameters. Then, the optimal high and low resonance components, which include the fault characteristic signals of rotating machinery, can be obtained by using the resonance-based signal spares decomposition method with the optimal decomposition parameters. Finally, the high and the low resonance components are subject to the Hilbert transform demodulation analysis; the faults of rotating machinery can be diagnosed based on the obtained envelop spectra. The optimal resonance-based signal spares decomposition method is successfully applied to the analysis of the simulation and experiment vibration signals. The analysis results demonstrate that the proposed method can successfully extract the fault features in rotating machinery.
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Tong, Shuiguang, Yidong Zhang, Jian Xu, and Feiyun Cong. "Pattern recognition of rolling bearing fault under multiple conditions based on ensemble empirical mode decomposition and singular value decomposition." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 12 (June 26, 2017): 2280–96. http://dx.doi.org/10.1177/0954406217715483.
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In rotating machinery, the malfunctions of rolling bearings are one of the most common faults. To prevent machine breakdown, the pattern recognition of rolling bearing faults has been a pivotal issue for fault identification and classification. This study proposes a new feature extraction method based on ensemble empirical mode decomposition (EEMD) and singular value decomposition (SVD) for fault classification. The proposed E–S method (EEMD combined with SVD using feature parameters) intends to enhance the faults identification capability in different working conditions, including various fault types (FT), fault severities (FS), and fault loads (FL). In this study, the E–S method is adopted to analyze the simulated signals. And the experiment further discusses three cases of different FT, FS, and FL separately under six different classifiers. The experimental results show that different fault classes can be effectively distinguished by the proposed E–S in comparison with other traditional feature extraction methods. Hence, the proposed method is verified to have an effective and excellent performance in bearing fault classification.
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Jing, Liuming, Lei Xia, Tong Zhao, and Jinghua Zhou. "An Improved Arc Fault Location Method of DC Distribution System Based on EMD-SVD Decomposition." Applied Sciences 13, no. 16 (August 10, 2023): 9132. http://dx.doi.org/10.3390/app13169132.
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The influence of the control strategy of the power electronic converter obscures the fault characteristics of DC distribution networks. The existence of arc faults over an extended period of time poses a grave threat to the security of power grids and may result in electric shock, fire, and other catastrophes. In recent years, the method of fault localization based on the traveling wave method has been a popular topic of research in the field of DC distribution system protection. In this paper, the fault localization principle of the traveling wave method is described in depth, and the propagation characteristics of the traveling wave of fault current in the online mode network are deduced. We present a method for wave head calibration that combines empirical mode decomposition (EMD) and singular value decomposition (VMD). After the fault-traveling current signal has been subjected to EMD, the first eigenmode function is extracted and subjected to singular value decomposition (SVD). After SVD, the detail component can reflect the singularity of the signal. The point of the maximum value of the detail component signal corresponds to the moment when the faulty traveling wave head reaches the monitoring point. Finally, the DC distribution system is modeled based on the PSCAD/EMTDC simulation environment, and the fault location method is verified. The simulation results show that the method can effectively realize fault localization.
7
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 (October 13, 2023): 2969. http://dx.doi.org/10.3390/pr11102969.
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Traditional methods for identifying gear faults typically require a substantial number of faulty samples, which in reality are challenging to obtain. To tackle this challenge, this paper introduces a sophisticated approach for intelligent gear fault identification, utilizing discrete wavelet decomposition and an enhanced convolutional neural network (CNN) optimized for scenarios with limited sample data. Initially, the features of the sample signal are extracted and enhanced using discrete wavelet decomposition. Subsequently, the refined signal is transformed into a two-dimensional image through a Markov transition field, preparing it for improved two-dimensional CNN training. Finally, the refined network model is applied to assess the gear fault dataset, achieving a training accuracy of 97% and a classification accuracy of 88.33%. This demonstrates the method’s feasibility and effectiveness in identifying gear faults with limited sample data.
8
Liao, Zhiqiang, Xuewei Song, Baozhu Jia, and Peng Chen. "Automatic Bearing Fault Feature Extraction Method via PFDIC and DBAS." Mathematical Problems in Engineering 2021 (May 25, 2021): 1–13. http://dx.doi.org/10.1155/2021/6655081.
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Determining the embedded dimension of a singular value decomposition Hankel matrix and selecting the singular values representing the intrinsic information of fault features are challenging tasks. Given these issues, this work presents a singular value decomposition-based automatic fault feature extraction method that uses the probability-frequency density information criterion (PFDIC) and dual beetle antennae search (DBAS). DBAS employs embedded dimension and singular values as dynamic variables and PFDIC as a two-stage objective to optimize the best parameters. The optimization results work for singular value decomposition for bearing fault feature extraction. The extracted fault signals combined with envelope demodulation can efficiently diagnose bearing faults. The superiority and applicability of the proposed method are validated by simulation signals, engineering signals, and comparison experiments. Results demonstrate that the proposed method can sufficiently extract fault features and accurately diagnose faults.
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Dou, Chun Hong. "Fault Feature Extraction for Gearboxes Using Empirical Mode Decomposition." Advanced Materials Research 383-390 (November 2011): 1376–80. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1376.
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The paper uses empirical mode decomposition to extract the fault feature of gearboxes. Traditional techniques fail to process the non-stationary and nonlinear signals. Empirical mode decomposition is a powerful tool for the non-stationary and nonlinear signal analysis and has attracted considerable attention recently. First, a simulation signal is used to measure the performance of the empirical mode decomposition method. Then, the empirical mode decomposition method is applied to analyze the signals captured from the gearbox with multiple faults and successfully extracts the multiple fault information from the collected signals. The results show that empirical mode decomposition could be a helpful method for mechanical fault feature extraction.
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Zhao, Nanyang, Zhiwei Mao, Donghai Wei, Haipeng Zhao, Jinjie Zhang, and Zhinong Jiang. "Fault Diagnosis of Diesel Engine Valve Clearance Based on Variational Mode Decomposition and Random Forest." Applied Sciences 10, no. 3 (February 7, 2020): 1124. http://dx.doi.org/10.3390/app10031124.
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Diesel engines, as power equipment, are widely used in the fields of the automobile industry, ship industry, and power equipment. Due to wear or faulty adjustment, the valve train clearance abnormal fault is a typical failure of diesel engines, which may result in the performance degradation, even valve fracture and cylinder hit fault. However, the failure mechanism features mainly in the time domain and angular domain, on which the current diagnosis methods are based, are easily affected by working conditions or are hard to extract accurate enough from, as the diesel engine keeps running in transient and non-stationary processes. This work aimed at diagnosing this fault mainly based on frequency band features, which would change when the valve clearance fault occurs. For the purpose of extracting a series of frequency band features adaptively, a decomposition technique based on improved variational mode decomposition was investigated in this work. As the connection between the features and the fault was fuzzy, the random forest algorithm was used to analyze the correspondence between features and faults. In addition, the feature dimension was reduced to improve the operation efficiency according to importance score. The experimental results under variable speed condition showed that the method based on variational mode decomposition and random forest was capable to detect the valve clearance fault effectively.
11
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 (April 17, 2016): 864–70. http://dx.doi.org/10.1177/0954408916644271.
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A new time–frequency analysis method, based on variational mode decomposition, was investigated. When a gear fault occurs, its vibration signal is nonstationary, nonlinear, and exhibits complex modulation performance. According to the modulation characteristics of the gear vibration signal arising from faults therein, a gear fault diagnosis method based on variational mode decomposition and envelope analysis was proposed. The variational mode decomposition method can decompose a complex signal into several stable components. The obtained components were analyzed by envelope demodulation. According to the envelope spectrum, gear faults can be diagnosed. In essence, the variational mode decomposition method can decompose a multi-component signal into a number of single component amplitude modulation–frequency modulation signals. The method is suited to the handling of multi-component amplitude modulation–frequency modulation signals. The simulated signal and the actual gear fault vibration signals were analyzed. The results showed that the method can be effectively applied to gear fault diagnosis.
12
Wang, Xiaoqian, Dali Sheng, Jinlian Deng, Wei Zhang, Jie Cai, Weisheng Zhao, and Jiawei Xiang. "Kernel Regression Residual Decomposition Method to Detect Rolling Element Bearing Faults." Mathematical Problems in Engineering 2021 (April 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/5523098.
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The raw vibration signal carries a great deal of information representing the mechanical equipment's health conditions. However, in the working condition, the vibration response signals of faulty components are often characterized by the presence of different kinds of impulses, and the corresponding fault features are always immersed in heavy noises. Therefore, signal denoising is one of the most important tasks in the fault detection of mechanical components. As a time-frequency signal processing technique without the support of the strictly mathematical theory, empirical mode decomposition (EMD) has been widely applied to detect faults in mechanical systems. Kernel regression (KR) is a well-known nonparametric mathematical tool to construct a prediction model with good performance. Inspired by the basic idea of EMD, a new kernel regression residual decomposition (KRRD) method is proposed. Nonparametric Nadaraya–Watson KR and a standard deviation (SD) criterion are employed to generate a deep cascading framework including a series of high-frequency terms denoted by residual signals and a final low-frequency term represented by kernel regression signal. The soft thresholding technique is then applied to each residual signal to suppress noises. To illustrate the feasibility and the performance of the KRRD method, a numerical simulation and the faulty rolling element bearings of well-known open access data as well as the experimental investigations of the machinery simulator are performed. The fault detection results show that the proposed method enables the recognition of faults in mechanical systems. It is expected that the KRRD method might have a similar application prospect of EMD.
13
Ao, HungLinh, Junsheng Cheng, Kenli Li, and Tung Khac Truong. "A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM." Shock and Vibration 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/825825.
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This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.
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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 (April 30, 2024): 2878. http://dx.doi.org/10.3390/s24092878.
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Sensor faults are one of the most common faults that cause performance degradation or functional loss in permanent magnet traction drive systems (PMTDSs). To quickly diagnose faulty sensors, this paper proposes a real-time joint diagnosis method for multi-sensor faults based on structural analysis. Firstly, based on limited monitoring signals on board, a structured model of the system was established using the structural analysis method. The isolation and detectability of faulty sensors were analyzed using the Dulmage–Mendelsohn decomposition method. Secondly, the minimum collision set method was used to calculate the minimum overdetermined equation set, transforming the higher-order system model into multiple related subsystem models, thereby reducing modeling complexity and facilitating system implementation. Next, residual vectors were constructed based on multiple subsystem models, and fault detection and isolation strategies were designed using the correlation between each subsystem model and the relevant sensors. The validation results of the physical testing platform based on online fault data recordings showed that the proposed method could achieve rapid fault detection and the localization of multi-sensor faults in PMTDS and had a good application value.
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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 (August 15, 2023): 3460. http://dx.doi.org/10.3390/electronics12163460.
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This study addresses the challenges of limited fault samples, noise interference, and low accuracy in existing fault diagnosis methods for three-phase inverters under real acquisition conditions. To increase the number of samples, Wavelet Packet Decomposition (WPD) denoising and a Conditional Variational Auto-Encoder (CVAE) are used for sample enhancement based on the existing faulty samples. The resulting dataset is then normalized, pre-processed, and used to train an improved deep residual network (SE-ResNet18) fault diagnosis model with a channel attention mechanism. Results show that the augmented fault samples improve the diagnosis accuracy compared with the original samples. Furthermore, the SE-ResNet18 model achieves higher fault diagnosis accuracy with fewer iterations and faster convergence, indicating its effectiveness in accurately diagnosing inverter open-circuit faults across various sample situations.
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Liu, Xinyue, Yan Yan, Kaibo Hu, Shan Zhang, Hongjie Li, Zhen Zhang, and Tingna Shi. "Fault Diagnosis of Rotor Broken Bar in Induction Motor Based on Successive Variational Mode Decomposition." Energies 15, no. 3 (February 7, 2022): 1196. http://dx.doi.org/10.3390/en15031196.
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When an induction motor is running at stable speed and low slip, the fault signal of the induction motor’s broken bar faults are easily submerged by the power frequency (50 Hz) signal. Thus, it is difficult to extract fault characteristics. The left-side harmonic component representing the fault characteristics can be distinguished from power frequency owing to V-shaped trajectory of the fault component in time-frequency (t-f) domain during motor startup. This article proposed a scheme to detect broken bar faults and discriminate the severity of faults under starting conditions. In this scheme, successive variable mode decomposition (SVMD) is applied to analyze the stator starting current to extract the fault component, and the signal reconstruction is proposed to maximize the energy of the fault component. Then, the quadratic regression curve method of instantaneous frequency square value of the fault component is utilized to discriminate whether the fault occurs. In addition, according to the feature that the energy of the fault component increases with the fault severity, the energy of the right part of the fault component is proposed to detect the severity of the fault. In this paper, experiments are carried out based on a 5.5 kW three-pole induction motor. The results show that the scheme proposed in this paper can diagnose the broken bar faults and determine the severity of the fault.
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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 (January 1, 2024): 012037. http://dx.doi.org/10.1088/1742-6596/2683/1/012037.
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Abstract A fault location method for flexible DC distribution network lines is proposed to address the issues of insufficient decomposition in fault signal extraction and the complexity of traveling wave head detection. The method combines adaptive noise empirical mode decomposition (CEEMDAN) and Teager energy operator (TEO). Firstly, CEEMDAN is used to adaptively denoise the line mode signal and obtain the optimal modal component to overcome modal aliasing and insufficient decomposition in signal separation. Then, the reach time of the fault traveling wave is determined by using TEO. Ultimately, the fault location is accurately determined by employing both ends of the traveling wave ranging technique. Through the combination of software PSCAD and MATLAB, the simulation experiment of the line fault is carried out. This means that the method can quickly and precisely identify the location of faults in a system.
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Li, Hui. "Fault Diagnosis of Gear Wear Based on Local Mean Decomposition." Advanced Materials Research 459 (January 2012): 298–302. http://dx.doi.org/10.4028/www.scientific.net/amr.459.298.
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A new approach to fault diagnosis of gear wear based on Local mean decomposition (LMD) is proposed. Local mean decomposition can adaptively decomposes the vibration signal into a series of product functions (PFs), which is the product of an envelope signal and a frequency modulated signal. LMD is capable of revealing interesting feature embedded in the signal. The experimental examples are conducted to evaluate the effectiveness of the proposed approach. The experimental results provide strong evidence that the performance of the approach based on local mean decomposition is better to extract the fault characteristics of the faulty gear and can effectively diagnose the gear wear fault.
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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 (February 1, 2017): 200–206. http://dx.doi.org/10.1177/1748006x17693492.
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Variational mode decomposition is a new signal decomposition method, which can process non-linear and non-stationary signals. It can overcome the problems of mode mixing and compensate for the shortcomings in empirical mode decomposition. Permutation entropy is a method which can detect the randomness and kinetic mutation behavior of a time series. It can be considered for use in fault diagnosis. The complexity of wind power generation systems means that the randomness and kinetic mutation behavior of their vibration signals are displayed at different scales. Multi-scale permutation entropy analysis is therefore needed for such vibration signals. This research investigated a method based on variational mode decomposition and permutation entropy for the fault diagnosis of a wind turbine roller bearing. Variational mode decomposition was adopted to decompose the bearing vibration signal into its constituent components. The components containing key fault information were selected for the extraction of their permutation entropy. This entropy was used as a bearing fault characteristic value. The nearest neighbor algorithm was employed as a classifier to identify faults in a roller bearing. The experimental data showed that the proposed method can be applied to wind turbine roller bearing fault diagnosis.
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Li, Hui. "Local Mean Decomposition Based Bearing Fault Detection." Advanced Materials Research 490-495 (March 2012): 360–64. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.360.
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A novel method of bearing fault diagnosis based on local mean decomposition (LMD) is proposed. LMD method is self-adaptive to non-stationary and non-linear signal. LMD can adaptively decompose the vibration signal into a series of product functions (PFs), which is the product of an envelope signal and a frequency modulated signal. Then the envelope spectrum is applied to the selected product function that stands for the bearing faults. Therefore, the character of the bearing fault can be recognized according to the envelope spectrum of product function. The experimental results show that local mean decomposition based envelope spectrum can effectively detect and diagnose bearing inner and outer race fault under strong background noise condition.
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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 (LMD) decomposes the signals into different physical fictions (PF). PFs are input into the matrix sample entropy based on Euclidean distance (MESE), and the PFs which best reflect fault characteristics are selected. Finally, the PFs by MESE are used to train the CNN to identify the faults of parallel-shaft gearbox. Experiment shows that MESE can quickly and accurately select the PFs with the most significant fault features. 1-D CNN can get nearly 100% recognition rate with less time and the CNN of SoftMax improved can effectively eliminate LMD endpoint effect. This method can successfully identify single faults, combination faults, and faults under different loads of the gearbox. Compared with other methods, this method has the characteristics of high efficiency, accuracy, and strong anti-interference. Therefore, it can effectively solve the problem of complex fault signal decomposition of gearbox and can diagnose the gearbox fault under different load operation. It has great significance for gearbox fault diagnosis in actual production.
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Wang, HongChao, and WenLiao Du. "Intelligent diagnosis of rolling bearing compound faults based on device state dictionary set sparse decomposition feature extraction–hidden Markov model." Advances in Mechanical Engineering 12, no. 6 (June 2020): 168781402093046. http://dx.doi.org/10.1177/1687814020930469.
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Identification of rolling bearing fault patterns, especially for the compound faults, has attracted notable attention and is still a challenge in fault diagnosis. Intelligent diagnosis method is an effective method for compound faults of rolling element bearing, and effective fault feature extraction is the key step to decide the intelligent diagnosis result to some extent. The sparse decomposition method could capture the complex impulsive characteristic components of rolling bearing more effectively than the other time–frequency analysis method when compound fault arises in rolling bearing. Based on the self-learning dictionary under different operating states of the device corresponding to the special features modes, an intelligent diagnosis method of rolling bearing compound faults based on device state dictionary set sparse decomposition feature extraction–hidden Markov model is proposed in the article. First, characteristic dictionaries of rolling bearing under different operating conditions are extracted by sparse decomposition self-learning method, and state dictionary set of rolling bearing is constructed. Then, the compound fault signals of bearing are transformed into sparse domain using the constructed dictionary set to extract sparse features. At last, the extracted sparse features are used as training and testing vectors of hidden Markov model, and satisfactory intelligent diagnosis results are obtained. The validity of the proposed method is verified by compound faults of rolling element bearing. In addition, the advantages of the proposed method are also verified by comparing with the other feature extraction and intelligent diagnosis methods, and the proposed method provides a feasible and efficient solution for fault diagnosis of rolling bearing compound faults.
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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 (August 3, 2016): 39–49. http://dx.doi.org/10.1177/1475921716661310.
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Rolling bearings, as important machinery components, strongly affect the operation of machines. Early bearing fault diagnosis methods commonly take time–frequency analysis as the fundamental basis, therein searching for characteristic fault frequencies based on bearing kinematics to identify fault locations. However, due to mode mixing, the characteristic frequencies are usually masked by normal frequencies and thus are difficult to extract. After time–frequency decomposition, the impact signal frequency can be distributed among multiple separation functions according to the mode mixing caused by the impact signal; therefore, it is possible to search for the shared frequency peak value in these separation functions to diagnose bearing faults. Using the wavelet transform, time–frequency analysis and blind source separation theory, this article presents a new method of determining shared frequencies, followed by identifying the faulty parts of bearings. Compared to fast independent component analysis, the sparse component analysis was better able to extract fault characteristics. The numerical simulation and the practical application test in this article obtained satisfactory results when combining the wavelet transform, intrinsic time-scale decomposition and linear clustering sparse component analysis, thereby proving the validity of this method.
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Akolkar, S. M., and H. R. Jariwala. "Highly efficient relay triggering circuit for fault detection during Power swings." Journal of Applied Research and Technology 22, no. 1 (February 29, 2024): 52–58. http://dx.doi.org/10.22201/icat.24486736e.2024.22.1.2120.
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This paper introduces a Discrete Wavelet Transform based very simple and fast acting algorithm with multi-resolution analysis to sense all types of faults in presence of power swings using current signal analyzation. The algorithm confirms very quick and efficient detection of various fault types in the first signal decomposition level of signal. The novelty of proposed algorithm lies in use of special type of Battle Lemarie mother wavelet having an advantage of perfect symmetry ensuring decomposition into B-Spline or same order polynomials capturing excellent speed and time-frequency localization of signal. The algorithm is tested for different fault parameters such as fault resistance, fault distance and time of initiation of faults considering EHV double circuit transmission line network and IEEE 9 Bus system developed in MATLAB environment. The proposed algorithm is capable of detecting all types of faults consistently within minimum time of 0.001 sec.
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Chen, Yong Hui, Xue Liang Zhang, and Hai Hong Li. "Feature Extraction of Nonstationarity Vibration Signal Based on Wavelet Decomposition." Applied Mechanics and Materials 220-223 (November 2012): 2228–34. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2228.
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Abstract. Nonstationarity feature representation and extraction method based on the wavelet decomposition and demodulation techniques are studied. Some component in special frequency band included faulty information is selected to reconstruct by wavelet analysis. The mono-components with fault feature in different frequency band would be captured and separated out. The demodulated and spectrally signals are analyzed by Hilbert transform, and it presents an approach to get the characteristic frequency of fault signals. So what kind of the fault mode is can be estimated. For the nonstationarity and modulation feature of rolling bearing fault signals, wavelet decomposition combined with Hilbert transform is effective in identifying the localized defects of rolling bearings.
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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 (October 11, 2020): 7058. http://dx.doi.org/10.3390/app10207058.
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Tooth spalling is one of the most destructive surface failure models of the gear faults. Previous studies have mainly concentrated on the spalling damage of a single gear tooth, but the spalling distributed over double teeth, which usually occurs in practical engineering problems, is rarely reported. To remedy this deficiency, this paper constructs a new dynamical model of a gear system with double-teeth spalling fault and validates this model with various experimental tests. The dynamic characteristics of gear systems are obtained by considering the excitations induced by the number of spalling teeth, and the relative position of two faulty teeth. Moreover, to ensure the accuracy of dynamic model verification results and reduce the difficulty of fault feature analysis, a novel parameter-adaptive variational mode decomposition (VMD) method based on the ant lion optimization (ALO) is proposed to eliminate the background noise from the experimental signal. First, the ALO is used for the self-selection of the decomposition number K and the penalty factor â of the VMD. Then, the raw signal is decomposed into a set of Intrinsic Mode Functions (IMFs) by applying the ALO-VMD, and the IMFs whose effective weight kurtosis (EWK) is greater than zero are selected as the reconstructed signal. Combined with envelope spectrum analysis, the de-nosing ability of the proposed method is compared with that of the method known as particle swarm optimization-based variational mode decomposition (PSO-VMD), the fixed-parameter VMD, the empirical mode decomposition (EMD), and the local mean decomposition (LMD), respectively. The results indicate that the proposed dynamic model and background elimination method can provide a theoretical basis for spalling defect diagnosis of gear systems.
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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 (August 10, 2016): 4186–96. http://dx.doi.org/10.1177/0954406216663782.
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Achieving a precise fault diagnosis for rolling bearings under variable conditions is a problematic challenge. In order to enhance the classification and achieves a higher precision for diagnosing rolling bearing degradation, a hybrid method is proposed. The method combines wavelet packet transform, singular value decomposition and support vector machine. The first step of the method is the decomposition of the signal using wavelet packet transform and then instantaneous amplitudes and energy are computed for each component. The Second step is to apply the singular value decomposition to the matrix constructed by the instantaneous amplitudes and energy in order to reduce the matrix dimension and obtaining the fault feature unaffected by the operating condition. The features extracted by singular value decomposition are then used as an input to the support vector machine in order to recognize the fault mode of rolling bearings. The method is applied to a bearing with faults created using electro-discharge machining under laboratory conditions. Test results show that the proposed methodology is effective to classify rolling bearing faults with high accuracy.
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Zhu, Wenwei, Chenyang Fan, Chenghao Xu, Hantuo Dong, Jingen Guo, Aiwu Liang, and Long Zhao. "Anchor Fault Identification Method for High-Voltage DC Submarine Cable Based on VMD-Volterra-SVM." Energies 16, no. 7 (March 27, 2023): 3053. http://dx.doi.org/10.3390/en16073053.
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This article introduces a new method for identifying anchor damage faults in fiber composite submarine cables. The method combines the Volterra model of Variation Mode Decomposition (VMD) with singular value entropy to improve the accuracy of fault identification. First, the submarine cable vibration signal is decomposed into various Intrinsic Mode Functions (IMFs) using VMD. Then, a Volterra adaptive prediction model is established by reconstructing the phase space of each IMF, and the model parameters are used to form an initial feature vector matrix. Next, the feature vector matrix is subjected to singular value decomposition to extract the singular value entropy that reflects the fault characteristics of the submarine cable. Finally, singular value entropy is used as a feature value to input into the Support Vector Machine (SVM) for classification. Compared with Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD), the proposed method achieves a higher fault identification accuracy and effectively identifies anchor damage faults in submarine cables. The results of this study demonstrate the feasibility and practicality of the proposed method.
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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 (November 27, 2014): 1550021. http://dx.doi.org/10.1142/s0218126615500218.
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This paper introduces a method for detection and identification of IGBT-based drive open-circuit fault of DTC induction motor drives. The detection mechanism is based on soft set theory and wavelet decomposition, if it is detailed, ⊼-product decision making method and sym2 wavelet decomposition have been used in the detection mechanism. In this method, the stator currents have been used as an input to the system. The stator current has been used for the detection of the fault. The signal analysis has been performed up to the six level details wavelets decomposition. Faulty switch is detected by applying soft set theory to sixth level wavelets transformation. This is the first time applied to inverter in induction motor drives fault detection. The results demonstrate that the proposed fault detection and diagnosis system has very good capabilities.
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Gao, Shuzhi, Ning Zhao, Xuefeng Chen, Zhiming Pei, and Yimin Zhang. "A new approach to adaptive VMD based on SSA for rolling bearing fault feature extraction." Measurement Science and Technology 35, no. 3 (December 8, 2023): 036102. http://dx.doi.org/10.1088/1361-6501/ad11cc.
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Abstract Due to the structure of rolling bearings, will have various problems. So the early detection of rolling bearing faults is very important. Consequently, a precise method for extracting fault features is required. In this study, an adaptive variational modal decomposition (VMD) fault feature extraction method is proposed, utilizing the sparrow search algorithm (SSA). Firstly, a novel measurement index called impulse diversity entropy (IDE) is introduced, which better represents internal changes within the mode components. Secondly, the SSA is employed to select the optimal VMD decomposition parameters based on the IDE. Finally, a spectrum analysis is conducted on the mode component with the highest IDE to extract fault features. The experimental results show that this method has an accurate feature extraction ability and obvious advantages over other methods in distinguishing fault and interference frequencies because it is a special signal decomposition method.
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Li, Quanfu, Yuxuan Zhou, Gang Tang, Chunlin Xin, and Tao Zhang. "Early Weak Fault Diagnosis of Rolling Bearing Based on Multilayer Reconstruction Filter." Shock and Vibration 2021 (March 2, 2021): 1–15. http://dx.doi.org/10.1155/2021/6690966.
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The early weak fault characteristics of rolling bearings are extremely weak and are easily overwhelmed by other noises. In order to effectively extract the characteristics of the early weak faults of the rolling bearings and draw on the multilayer wavelet decomposition idea, a method for diagnosing the early weak faults of the rolling bearing based on the multilayer reconstruction filter is proposed. As we all know, empirical wavelet transform (EWT) makes full use of wavelet filter bank, and variational mode decomposition (VMD) uses Wiener filter bank. This paper fully combines the advantages of the above two methods, adaptively determines the number of modes through empirical wavelet decomposition and divides the original signal, extracts the frequency band that contains the fault characteristic information, and effectively eliminates noise interference. These steps are repeated until the optimal component of the condition is obtained. In the output layer, the weak fault impact components are further separated by the strong filtering and signal decomposition capability of VMD. The advantages of the proposed method are proved by the experiment of weak fault of rolling bearing and the accelerated failure experiment of full life. The proposed method has the advantages of reducing noise influence and adaptive estimation of decomposed modes, which can be applied more efficiently in practice.
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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 (October 24, 2019): 1770–81. http://dx.doi.org/10.1108/compel-03-2019-0101.
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Purpose The purpose of this paper is to develop a method for multiple soft fault diagnosis of nonlinear circuits including fault detection, identification of faulty elements and estimation of their values in real circumstances. Design/methodology/approach The method for fault diagnosis proposed here uses a measurement test leading to a system of nonlinear equations expressing the measured quantities in terms of the circuit parameters. Nonlinear functions, which appear in these equations are not given in explicit analytical form. The equations are solved using a homotopy concept. A key problem of the solvability of the equations is considered locally while tracing the solution path. Actual faults are selected on the basis of the observation that the probability of faults in fewer number of elements is greater than in a larger number of elements. Findings The results indicate that the method is an effective tool for testing nonlinear circuits including bipolar junction transistors and junction field effect transistors. Originality/value The homotopy method is generalized and associated with a restart procedure and a numerical algorithm for solving differential equations. Testable sets of elements are found using the singular value decomposition. The procedure for selecting faulty elements, based on the minimal fault number rule, is developed. The method comprises both theoretical and practical aspects of fault diagnosis.
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Cheng, Xiaohan, Hui Yang, Long Yuan, Yuxin Lu, Congjie Cao, and Guangqiang Wu. "Fault Feature Enhanced Extraction and Fault Diagnosis Method of Vibrating Screen Bearings." Machines 10, no. 11 (November 1, 2022): 1007. http://dx.doi.org/10.3390/machines10111007.
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For mechanical equipment, bearings have a high incidence area of faults. A problem for bearings is that their fault characteristics include a vibrating screen exciter which is weak and thus easily covered in strong background noise, hence making the noise difficult to remove. In this paper, a noise reduction method based on singular value decomposition, improved by singular value’s unilateral ascent method (SSVD), and a fault feature enhancement method, i.e., variational mode decomposition, improved by revised whale algorithm optimization (RWOA-VMD), are proposed. These two methods are used in vibration signal processing with early faults of bearings having a vibrating screen and they have achieved significant application results. This paper also aims to construct a multi-modal feature matrix composed of energy entropy, singular value entropy, and power spectrum entropy, and then the early fault diagnosis of bearings of a vibrating screen exciter bearing is realized by using the proposed support vector machine, improved by the aquila optimizer algorithm (AO-SVM).
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Yu, Ming, Chenyu Xiao, Hai Wang, Wuhua Jiang, and Rensheng Zhu. "Adaptive Cuckoo Search-Extreme Learning Machine Based Prognosis for Electric Scooter System under Intermittent Fault." Actuators 10, no. 11 (October 22, 2021): 283. http://dx.doi.org/10.3390/act10110283.
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In this paper, an adaptive Cuckoo search extreme learning machine (ACS-ELM)-based prognosis method is developed for an electric scooter system with intermittent faults. Firstly, bond-graph-based fault detection and isolation is carried out to find possible faulty components in the electric scooter system. Secondly, submodels are decomposed from the global model using structural model decomposition, followed by adaptive Cuckoo search (ACS)-based distributed fault estimation with less computational burden. Then, as the intermittent fault gradually deteriorates in magnitude, and possesses the characteristics of discontinuity and stochasticity, a set of fault features that can describe the intermittent fault’s evolutionary trend are captured with the aid of tumbling window. With the obtained dataset, which represents the fault features, the ACS-ELM is developed to model the intermittent fault degradation trend and predict the remaining useful life of the intermittently faulty component when the physical degradation model is unavailable. In the ACS-ELM, the ACS is employed to optimize the input weights and hidden layer biases of an extreme learning machine, to improve the algorithm performance. Finally, the proposed methodologies are validated by a series of simulation and experiment results based on the electric scooter system.
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Bi, Xiaobo, Jiansheng Lin, Daijie Tang, Fengrong Bi, Xin Li, Xiao Yang, Teng Ma, and Pengfei Shen. "VMD-KFCM Algorithm for the Fault Diagnosis of Diesel Engine Vibration Signals." Energies 13, no. 1 (January 2, 2020): 228. http://dx.doi.org/10.3390/en13010228.
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Accurate and timely fault diagnosis for the diesel engine is crucial to guarantee it works safely and reliably, and reduces the maintenance costs. A novel diagnosis method based on variational mode decomposition (VMD) and kernel-based fuzzy c-means clustering (KFCM) is proposed in this paper. Firstly, the VMD algorithm is optimized to select the most suitable K value adaptively. Then KFCM is employed to classify the feature parameters of intrinsic mode functions (IMFs). Through the comparison of many different parameters, the singular value is selected finally because of the good classification effect. In this paper, the diesel engine fault simulation experiment was carried out to simulate various faults including valve clearance fault, fuel supply fault and common rail pressure fault. Each kind of machine fault varies in different degrees. To prove the effectiveness of VMD-KFCM, the proposed method is compared with empirical mode decomposition (EMD)-KFCM, ensemble empirical mode decomposition (EEMD)-KFCM, VMD-back propagation neural network (BPNN), and VMD-deep belief network (DBN). Results show that VMD-KFCM has advantages in accuracy, simplicity, and efficiency. Therefore, the method proposed in this paper can be used for diesel engine fault diagnosis, and has good application prospects.
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Zhong, Xianyou, Liu He, Gang Wan, and Yang Zhao. "Fault diagnosis for rolling bearings based on generalised dispersive mode decomposition and accugram." Insight - Non-Destructive Testing and Condition Monitoring 66, no. 2 (February 1, 2024): 74–81. http://dx.doi.org/10.1784/insi.2024.66.2.74.
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Bearing fault diagnosis helps to ensure the safe operation of electromechanical equipment and reduce unnecessary losses due to downtime. The interference of noise in the signal poses a challenge in the effective identification of rolling bearing faults. To address the above problems, this paper proposes a rolling bearing fault diagnosis (RBFD) method based on generalised dispersive mode decomposition (GDMD) and an accugram. Firstly, the bearing signal is decomposed using GDMD and the optimal number of decomposition modes is chosen using a new index based on the correlation coefficient and accuracy. According to the number of determined decomposition modes, the fault signal is reconstructed. Then, the centre frequency and bandwidth of the resonant frequency are determined using an accugram. Finally, the fault signal is filtered and analysed using a square envelope spectrum to achieve rolling bearing fault diagnosis. Experimental signal analysis verifies the effectiveness and feasibility of the method. The method is applied to the early fault diagnosis of rolling bearings and compared with kurtogram and accugram results. The results show that the approach can not only effectively avoid the interference of external impacts but it can also correctly recognise the fault characteristic frequency band.
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Camarena-Martinez, David, Jose R. Huerta-Rosales, Juan P. Amezquita-Sanchez, David Granados-Lieberman, Juan C. Olivares-Galvan, and Martin Valtierra-Rodriguez. "Variational Mode Decomposition-Based Processing for Detection of Short-Circuited Turns in Transformers Using Vibration Signals and Machine Learning." Electronics 13, no. 7 (March 26, 2024): 1215. http://dx.doi.org/10.3390/electronics13071215.
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Transformers are key elements in electrical systems. Although they are robust machines, different faults can appear due to their inherent operating conditions, e.g., the presence of different electrical and mechanical stresses. Among the different elements that compound a transformer, the winding is one of the most vulnerable parts, where the damage of turn-to-turn short circuits is one of the most studied faults since low-level damage (i.e., a low number of short-circuited turns—SCTs) can lead to the overall fault of the transformer; therefore, early fault detection has become a fundamental task. In this regard, this paper presents a machine learning-based method to diagnose SCTs in the transformer windings by using their vibrational response. In general, the vibration signals are firstly decomposed by means of the variational mode decomposition method, where a comparison with the empirical mode decomposition (EMD) method and the ensemble empirical mode decomposition (EEMD) method is also carried out. Then, entropy, energy, and kurtosis indices are obtained from each decomposition as fault indicators, where both the combination of features and the dimensionality reduction by using the principal component analysis (PCA) method are analyzed for the global effectiveness improvement and the computational burden reduction. Finally, a pattern recognition algorithm based on artificial neural networks (ANNs) is used for automatic fault detection. The obtained results show 100% effectiveness in detecting seven fault conditions, i.e., 0 (healthy), 5, 10, 15, 20, 25, and 30 SCTs.
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Cui, Lingli, Na Wu, Daiyi Mo, Huaqing Wang, and Peng Chen. "CQFB and PBP in Diagnosis of Local Gear Fault." Advances in Mechanical Engineering 6 (January 1, 2014): 670725. http://dx.doi.org/10.1155/2014/670725.
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The vibration signal of local gear fault is mainly composed of two components. One is the resonant signal and noise signal and the other one is the transient impulse signal including fault information. The quality factors corresponding to the two components are different. Hence, a method to diagnose local gear fault based on composite quality factor basis and parallel basis pursuit is proposed. First, two different quality factors bases are established using wavelet transform of variable quality factors to obtain the decomposition coefficient. Next, the parallel basis pursuit is adopted for the optimization of the decomposition coefficient. With the derived optimal decomposition coefficient, the resonant components with different quality factors can be reconstructed. By discussing the sparsity of signals treated with different quality factors bases, the suitable composite quality factor basis is selected to perform sparse decomposition on the signal. Besides, the obtained resonant component with low quality factor is subject to demodulation analysis, so as to derive the fault information. The feasibility and validity of the algorithm are shown by the results from simulation signal and practical application of local gear faults.
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Mishra, Saswati, Shubhrata Gupta, Anamika Yadav, and Almoataz Y. Abdelaziz. "Traveling Wave-Based Fault Localization in FACTS-Compensated Transmission Line via Signal Decomposition Techniques." Energies 16, no. 4 (February 14, 2023): 1871. http://dx.doi.org/10.3390/en16041871.
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Modern power systems are structurally complex and are vulnerable to undesirable events like faults. In the event of faults in transmission line, accurate fault location improves restoration process, thereby enhancing the reliability of the overall system. Fault location methods (FLMs) are tools which assist in identifying fault locations quickly. However, the accuracy of these FLMs gets affected in the presence of flexible alternating current transmission system (FACTS) devices. Therefore, in this work, the performance of four different signal decomposition techniques aided traveling wave aided FLMs are qualitatively compared in the context of fault localization in FACTS-compensated systems. FLMs based on intrinsic time decomposition (ITD), empirical mode decomposition (EMD), S-transform (ST), and estimation of signal parameters via rotational invariance technique (ESPRIT) are investigated. The accuracy of FLMs is tested for different cases of series, shunt, and series-shunt FACTS-compensated systems. A 500 kV system employed with 100 MVAr FACTS device is used for simulation. The instant of arrival wave at end of transmission line is from all aforementioned FLMs. The obtained ATWs are used in fault localization. Further, the associated percentage errors are calculated. The results suggest that EMD and ESPRIT-based FLMs are more accurate than others.
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Lin, Shih-Lin. "Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults." Sensors 21, no. 18 (September 10, 2021): 6065. http://dx.doi.org/10.3390/s21186065.
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Motor failure is one of the biggest problems in the safe and reliable operation of large mechanical equipment such as wind power equipment, electric vehicles, and computer numerical control machines. Fault diagnosis is a method to ensure the safe operation of motor equipment. This research proposes an automatic fault diagnosis system combined with variational mode decomposition (VMD) and residual neural network 101 (ResNet101). This method unifies the pre-analysis, feature extraction, and health status recognition of motor fault signals under one framework to realize end-to-end intelligent fault diagnosis. Research data are used to compare the performance of the three models through a data set released by the Federal University of Rio de Janeiro (UFRJ). VMD is a non-recursive adaptive signal decomposition method that is suitable for processing the vibration signals of motor equipment under variable working conditions. Applied to bearing fault diagnosis, high-dimensional fault features are extracted. Deep learning shows an absolute advantage in the field of fault diagnosis with its powerful feature extraction capabilities. ResNet101 is used to build a model of motor fault diagnosis. The method of using ResNet101 for image feature learning can extract features for each image block of the image and give full play to the advantages of deep learning to obtain accurate results. Through the three links of signal acquisition, feature extraction, and fault identification and prediction, a mechanical intelligent fault diagnosis system is established to identify the healthy or faulty state of a motor. The experimental results show that this method can accurately identify six common motor faults, and the prediction accuracy rate is 94%. Thus, this work provides a more effective method for motor fault diagnosis that has a wide range of application prospects in fault diagnosis engineering.
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Fei, Chun Guo, and Bai Li Su. "Overhead Line Fault Location Using Wavelet Packet Decomposition and Support Vector Regression." Advanced Materials Research 516-517 (May 2012): 1396–99. http://dx.doi.org/10.4028/www.scientific.net/amr.516-517.1396.
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This paper realizes the fault location in overhead line by using wavelet packet decomposition (WPD) and support vector regression (SVR). All various types of faults at different locations and various fault inception angles on a 735kV-360km overhead line power system are used. The system only utilizes voltage signals with single-end measurements. WPD is used to extract distinctive features from 1/2 cycle of post fault signals after noises have been eliminated by low pass filter. A SVR is trained with features obtained from WPD and consequently used in precise location of fault on the transmission line. The simulation results show, fault location on transmission line can be determined rapidly and correctly irrespective of fault impedance.
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Liu, Weixin, Yujia Wang, Baoji Yin, Xing Liu, and Mingjun Zhang. "Thruster fault identification based on fractal feature and multiresolution wavelet decomposition for autonomous underwater vehicle." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 13 (February 21, 2016): 2528–39. http://dx.doi.org/10.1177/0954406216632280.
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There exist some problems when the fractal feature method is applied to identify thruster faults for autonomous underwater vehicles (AUVs). Sometimes it could not identify the thruster fault, or the identification error is large, even the identification results are not consistent for the repeated experiments. The paper analyzes the reasons resulting in these above problems according to the experiments on AUV prototype with thruster faults. On the basis of these analyses, in order to overcome the above deficiency, an improved fractal feature integrated with wavelet decomposition identification method is proposed for AUV with thruster fault. Different from the fractal feature method where the signal extraction and fault identification are completed in the time domain, the paper makes use of the time-domain and frequent-domain information to identify thruster faults. In the paper, the thruster fault could be mapped multisource and described redundantly by the fault feature matrix constructed based on the time-domain and frequent-domain information. In the process of identification, different from the fractal feature method where the fault is identified based on fault identification model, the fault sample bank is built at first in the paper, and then pattern recognition is achieved by calculating the relative coefficients between the constructed fault feature matrix and the elements in the fault sample bank. Finally, the online pool experiments are performed on an AUV prototype, and the effectiveness of the proposed method is demonstrated in comparison with the fractal feature method.
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Yao, Gang, Yunce Wang, Mohamed Benbouzid, and Mourad Ait-Ahmed. "A Hybrid Gearbox Fault Diagnosis Method Based on GWO-VMD and DE-KELM." Applied Sciences 11, no. 11 (May 28, 2021): 4996. http://dx.doi.org/10.3390/app11114996.
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In this paper, a vibration signal-based hybrid diagnostic method, including vibration signal adaptive decomposition, vibration signal reconstruction, fault feature extraction, and gearbox fault classification, is proposed to realize fault diagnosis of general gearboxes. The main contribution of the proposed method is the combining of signal processing, machine learning, and optimization techniques to effectively eliminate noise contained in vibration signals and to achieve high diagnostic accuracy. Firstly, in the study of vibration signal preprocessing and fault feature extraction, to reduce the impact of noise and mode mixing problems on the accuracy of fault classification, Variational Mode Decomposition (VMD) was adopted to realize adaptive signal decomposition and Wolf Grey Optimizer (GWO) was applied to optimize parameters of VMD. The correlation coefficient was subsequently used to select highly correlated Intrinsic Mode Functions (IMFs) to reconstruct the vibration signals. With these re-constructed signals, fault features were extracted by calculating their time domain parameters, energies, and permutation entropies. Secondly, in the study of fault classification, Kernel Extreme Learning Machine (KELM) was adopted and Differential Evolutionary (DE) was applied to search its regularization coefficient and kernel parameter to further improve classification accuracy. Finally, gearbox vibration signals in healthy and faulty conditions were obtained and contrast experiences were conducted to validate the effectiveness of the proposed hybrid fault diagnosis method.
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Lu, Xinmiao, Zihan Lu, Qiong Wu, Jiaxu Wang, Cunfang Yang, Shuai Sun, Dan Shao, and Kaiyi Liu. "Soft Fault Diagnosis of Analog Circuit Based on EEMD and Improved MF-DFA." Electronics 12, no. 1 (December 27, 2022): 114. http://dx.doi.org/10.3390/electronics12010114.
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Aiming at the problems of nonlinearity and serious confusion of fault characteristics in analog circuits, this paper proposed a fault diagnosis method for an analog circuit based on ensemble empirical pattern decomposition (EEMD) and improved multifractal detrended fluctuations analysis (MF-DFA). This method consists of three steps: preprocessing, feature extraction, and fault classification identification. First, the EEMD decomposition preprocesses (denoises) the original signal; then, the appropriate IMF components are selected by correlation analysis; then, the IMF components are processed by the improved MF-DFA, and the fault feature values are extracted by calculating the multifractal spectrum parameters, and then the feature values are input to a support vector machine (SVM) for classification, which enables the diagnosis of soft faults in analog circuits. The experimental results show that the proposed EEMD-improved MF-DFA method effectively extracts the features of soft faults in nonlinear analog circuits and obtains a high diagnosis rate.
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Zhang, Xibo, Jianguo Ying, Junhua Wang, Rongwei Zhang, Zhou Hong, and Haibo Bi. "Design of fault degree diagnosis algorithm for circuit breaker spring based on fuzzy clustering." Journal of Physics: Conference Series 2724, no. 1 (March 1, 2024): 012009. http://dx.doi.org/10.1088/1742-6596/2724/1/012009.
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Abstract This paper proposes a design of a fault diagnosis algorithm for circuit breaker springs based on fuzzy clustering. The features of the fault state signal are extracted by combining the methods of Intrinsic Time-Scale Decomposition and Singular Spectrum Analysis. Using the fuzzy clustering method, this study classifies circuit breaker spring faults, extracts fault features, and achieves fault degree diagnosis. The experimental results show that the algorithm has high accuracy in fault diagnosis.
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Liu, Yan Li, De Xiang Zhang, and Ming Wei Ji. "Gearbox Fault Diagnosis Based on Empirical Mode Decomposition and Hilbert Transform." Advanced Materials Research 542-543 (June 2012): 238–41. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.238.
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Gearbox is vital components in a wide range of industrial and transport applications. It is very important how to monitor operating state of automobile gearbox and detect incipient faults. This paper applies the empirical mode decomposition (EMD) and Hilbert spectrum 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 Hilbert spectrum of intrinsic mode function at different fault characteristic frequencies is obtained by Hilbert transform. Finally, the time-frequency fault characteristics of gearbox are analyzed by the Hilbert spectrum value of intrinsic mode function. Experiment result has shown the feasibility and efficiency of the EMD algorithms and Hilbert spectrum characteristic method in fault diagnosis and fault message abstraction.
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Wu, Qin, Jun Niu, and Xinglian Wang. "Fault Feature Extraction Method of Ball Screw Based on Singular Value Decomposition, CEEMDAN and 1.5DTES." Actuators 12, no. 11 (November 7, 2023): 416. http://dx.doi.org/10.3390/act12110416.
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In this article, a method is proposed to effectively extract weak fault features and accurately diagnose faults in ball screws, even in the presence of strong background noise. This method combines singular value decomposition (SVD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the 1.5-dimensional spectrum (1.5D) to process and analyze fault vibration signals. The first step involves decomposing the fault signal using the SVD algorithm. The singular values are then screened, and the part of the screen containing more noise information is extracted to complete the first denoising step. The second step involves decomposing the signal after the initial denoising process using CEEMDAN and removing some of the false components from the intrinsic mode function (IMF) components, based on the kurtosis correlation function index. The signal is then reconstructed to complete the second denoising step. Finally, the denoised signal is analyzed using Teager energy operator demodulation and 1.5D spectral analysis to extract the fault frequency and determine the location of the fault in the ball screw. This method has been compared with other denoising methods, such as wavelet packet decomposition combined with CEEMDAN or SVD combined with variational mode decomposition (VMD), and the results show that under the condition of strong background noise, the proposed method can better extract the fault frequency of ball screw.
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Li, Zhen Tao, and Hui Li. "EMD and Envelope Spectrum Based Bearing Fault Detection." Advanced Materials Research 459 (January 2012): 233–37. http://dx.doi.org/10.4028/www.scientific.net/amr.459.233.
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A novel method to fault diagnosis of bearing based on empirical mode decomposition (EMD) and envelope spectrum is presented. EMD method is self-adaptive to non-stationary and non-linear signal. The methodology developed in this paper decomposes the original vibration signal in intrinsic oscillation modes, using the empirical mode decomposition. Then the envelope spectrum is applied to the selected intrinsic mode function that stands for the bearing faults. The basic principle is firstly introduced in detail. Then the EMD is applied in the research of the fault detection and diagnosis of the bearing. The experimental results show that the proposed method based on EMD and envelope spectrum analysis technique can effectively diagnose the faults of bearing.
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Fang, Xing, Hongxin Zhang, Danzhi Wang, Hao Yan, Fan Fan, and Lei Shu. "Algebraic Persistent Fault Analysis of SKINNY_64 Based on S_Box Decomposition." Entropy 24, no. 11 (October 22, 2022): 1508. http://dx.doi.org/10.3390/e24111508.
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Algebraic persistent fault analysis (APFA), which combines algebraic analysis with persistent fault attacks, brings new challenges to the security of lightweight block ciphers and has received widespread attention since its introduction. Threshold Implementation (TI) is one of the most widely used countermeasures for side channel attacks. Inspired by this method, the SKINNY block cipher adopts the S_box decomposition to reduce the number of variables in the set of algebraic equations and the number of Conjunctive Normal Form (CNF) equations in this paper, thus speeding up the algebraic persistent fault analysis and reducing the number of fault ciphertexts. In our study, we firstly establish algebraic equations for full-round faulty encryption,and then analyze the relationship between the number of fault ciphertexts required and the solving time in different scenarios (decomposed S_boxes and original S_box). By comparing the two sets of experimental results, the success rate and the efficiency of the attack are greatly improved by using S_box decomposition. In this paper, We can recover the master key in a minimum of 2000s using 11 pairs of plaintext and fault ciphertext, while the key recovery cannot be done in effective time using the original S_box expression equations. At the same time, we apply S_box decomposition to another kind of algebraic persistent fault analysis, and the experimental results show that using S_box decomposition can effectively reduce the solving time and solving success rate under the same conditions.
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Zhao, Qiao, Zengping Wang, Guomin Li, Xuanjun Liu, and Yuxuan Wang. "A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer." Applied Sciences 13, no. 10 (May 12, 2023): 5974. http://dx.doi.org/10.3390/app13105974.
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In this paper, a fault location method based on divide-and-conquer (DAC) is proposed to solve the inadequacy problem that arises when using the traditional fault section location method based on the optimization model of logic operation. The problem is that it is difficult to balance speed and accuracy after the scale of the distribution network is expanded. First, the causal link between fault information and the faulty device was described using the road vector, the equivalent transformation of the logical operations in the traditional model was implemented with the properties of the road vector, and the numerical computational model of the fault location was constructed. Based on this, the optimization-seeking variable “approximation gain” was introduced to prove that the proposed model conforms to the recursive structure of DAC, and the method of applying DAC to locate faults is proposed. The method applies the “Divide-Conquer-Combine” recursive mode to locate faults, and each level of recursion contains only linear-time “approximation gain” operations and constant-time decomposition and combination operations. The efficiency analysis and simulation results show that the proposed method has linear-time complexity and can accurately locate faults in milliseconds, providing a reference for solving the fault location problem in large distribution networks.