Relevant bibliographies by topics / Gear fault diagnosis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Gear fault diagnosis'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Gear fault diagnosis"

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Pi, Jun, Zhi Wei Li, and Guo Hua Yan. "Improved Fault Diagnosis Method for Aeroaccessory Gear." Advanced Materials Research 516-517 (May 2012): 718–21. http://dx.doi.org/10.4028/www.scientific.net/amr.516-517.718.

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The gear is widely used in aviation engines to transmit power. The gear faults affect somwhat the safety of the engines and aircafts. The vibration signal of gear is a carrier of gear situation information, it contains a lot of information about normal gear or faulty gear, so an effective signal process way is the important method of diagnosis the gear in good situation or not.The hybrid method of Wigner-Viller distribution (WVD) and singularity value decompositio(SVD) was introduced and applied to diagnose the gear faults in this paper. The results show that the hybrid method investigated is
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Zhang, Mingming, Jiangtian Yang, and Zhang Zhang. "Locomotive Gear Fault Diagnosis Based on Wavelet Bispectrum of Motor Current." Shock and Vibration 2021 (July 12, 2021): 1–12. http://dx.doi.org/10.1155/2021/5554777.

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The motor current signature analysis (MCSA) provides a nondestructive method for gear fault detection. The motor current in the faulty gear system not only involves the frequency information related to the fault but also the electric supply frequency and gear meshing-related frequency, which not only contaminates the fault characteristics but also increases the difficulty of fault extraction. To extract the fault characteristic frequency effectively, an innovative method based on the wavelet bispectrum (WB) is proposed. Bispectrum is an effective tool for identifying the fault-related quadrati
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Wu, Bin, Song He Zhang, Yue Gang Luo, and Shan Ping Yu. "Gear Fault Diagnosis Methods Based on EMD and HMM." Applied Mechanics and Materials 333-335 (July 2013): 1684–87. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1684.

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Due to the feature and the forms of motion of the gears, the vibration signal of the gear is mainly the frequency modulation, amplitude modulation, or hybrid modulation signal corresponding to the gear-mesh frequency and its double frequency signal. When faults arise on the gears, the number and shape of the modulation sideband will be changed. The structures and forms of the FM composition differ according to the type of faults. According to the above mentioned characteristic, this essay raises a method to disassemble the gear vibrate signal, points out the formulas to build up characteristic
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Tong, Shuiguang, Yuanyuan Huang, Zheming Tong, and Feiyun Cong. "A novel short-frequency slip fault energy distribution-based demodulation technique for gear diagnosis and prognosis." International Journal of Advanced Robotic Systems 17, no. 2 (2020): 172988142091503. http://dx.doi.org/10.1177/1729881420915032.

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To conduct diagnosis and prognosis of gears, this paper introduces a novel short-frequency slip fault energy distribution-based demodulation method. As an essential step of the method, the resonance-based sparse signal decomposition algorithm is firstly employed to obtain the high-resonance part from the raw gear fault signal. To deal with the difficulty in determining the resonance frequency band, we establish a multi-input signal-output model to describe the signal components acquired from a faulty gear. Based on it, the short-frequency slip fault energy distribution graph is defined to loca
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Buzzoni, M., E. Mucchi, G. D’Elia, and G. Dalpiaz. "Diagnosis of Localized Faults in Multistage Gearboxes: A Vibrational Approach by Means of Automatic EMD-Based Algorithm." Shock and Vibration 2017 (2017): 1–22. http://dx.doi.org/10.1155/2017/8345704.

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The gear fault diagnosis on multistage gearboxes by vibration analysis is a challenging task due to the complexity of the vibration signal. The localization of the gear fault occurring in a wheel located in the intermediate shaft can be particularly complex due to the superposition of the vibration signature of the synchronous wheels. Indeed, the gear fault detection is commonly restricted to the identification of the stage containing the faulty gear rather than the faulty gear itself. In this context, the paper advances a methodology which combines the Empirical Mode Decomposition and the Tim
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Han, Bing, Xiaohui Yang, Yafeng Ren, and Wanggui Lan. "Comparisons of different deep learning-based methods on fault diagnosis for geared system." International Journal of Distributed Sensor Networks 15, no. 11 (2019): 155014771988816. http://dx.doi.org/10.1177/1550147719888169.

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The running state of a geared transmission system affects the stability and reliability of the whole mechanical system. It will greatly reduce the maintenance cost of a mechanical system to identify the faulty state of the geared transmission system. Based on the measured gear fault vibration signals and the deep learning theory, four fault diagnosis neural network models including fast Fourier transform–deep belief network model, wavelet transform–convolutional neural network model, Hilbert-Huang transform–convolutional neural network model, and comprehensive deep neural network model are dev
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Sun, Shang Yuan, and Yang Wang. "Fault Diagnosis of Gear Box Based on BP Neural Network." Applied Mechanics and Materials 667 (October 2014): 349–52. http://dx.doi.org/10.4028/www.scientific.net/amm.667.349.

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The complexity of gear transmission condition makes a nonlinear mapping relationship between fault form and feature of it, the traditional signal processing methods is not easy to extract fault feature, it has caused great difficulties to gear fault diagnosis. This text is concerned with a class of fault diagnosis system of BP neural network to be used for fault diagnosis of gear. The simulation results show that the method can be used for the identification and diagnosis of gear faults.
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An, Xueli, Hongtao Zeng, and Chaoshun Li. "Envelope demodulation based on variational mode decomposition for gear fault diagnosis." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 231, no. 4 (2016): 864–70. http://dx.doi.org/10.1177/0954408916644271.

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A new time–frequency analysis method, based on variational mode decomposition, was investigated. When a gear fault occurs, its vibration signal is nonstationary, nonlinear, and exhibits complex modulation performance. According to the modulation characteristics of the gear vibration signal arising from faults therein, a gear fault diagnosis method based on variational mode decomposition and envelope analysis was proposed. The variational mode decomposition method can decompose a complex signal into several stable components. The obtained components were analyzed by envelope demodulation. Accor
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Tiwari, Dr Ashesh, and Himanshu Bhiwapurkar. "Fault Diagnosis of Gear box using Cepstrum Analysis." Indian Journal of Applied Research 3, no. 7 (2011): 206–8. http://dx.doi.org/10.15373/2249555x/july2013/64.

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Wen, Weigang, Robert X. Gao, and Weidong Cheng. "Planetary Gearbox Fault Diagnosis Using Envelope Manifold Demodulation." Shock and Vibration 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/3952325.

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The important issue in planetary gear fault diagnosis is to extract the dependable fault characteristics from the noisy vibration signal of planetary gearbox. To address this critical problem, an envelope manifold demodulation method is proposed for planetary gear fault detection in the paper. This method combines complex wavelet, manifold learning, and frequency spectrogram to implement planetary gear fault characteristic extraction. The vibration signal of planetary gear is demodulated by wavelet enveloping. The envelope energy is adopted as an indicator to select meshing frequency band. Man
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Dissertations / Theses on the topic "Gear fault diagnosis"

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Ahmaida, Anwar M. "Condition monitoring and fault diagnosis of a multi-stage gear transmission using vibro-acoustic signals." Thesis, University of Huddersfield, 2018. http://eprints.hud.ac.uk/id/eprint/34755/.

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Gearbox condition monitoring(CM) plays a vital role in ensuring the reliability and operational efficiency of a wide range of industrial facilities such as wind turbines and helicopters. Many technologies have been investigated intensively for more accurate CM of rotating machines with using vibro-acoustic signature analysis. However, a comparison of CM performances between surface vibrations and airborne acoustics has not been carried out with the use of emerging signal processing techniques. This research has focused on a symmetric evaluation of CM performances using vibrations obtained from
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Feki, Nabih. "Modélisation électro-mécanique de transmissions par engrenages : Applications à la détection et au suivi des avaries." Phd thesis, INSA de Lyon, 2012. http://tel.archives-ouvertes.fr/tel-00743722.

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La détection et le diagnostic des défauts d'engrenages ont été traditionnellement basés sur l'analyse des signaux vibratoires et acoustiques. Mais, ces méthodes peuvent être coûteuses suite aux difficultés techniques de mise en œuvre de capteurs sur des pièces en rotation. Dans ces travaux de thèse, une méthode originale de détection de défauts locaux dans des engrenages entraînés par des moteurs électriques est proposée en se basant sur le suivi des courants statoriques. Le système électromécanique est simulé numériquement en combinant un modèle électrique dynamique de moteurs asynchrones (mo
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Moraes, Matheus de. "Validação de um modelo dinâmico realístico de um par engrenado aplicado no monitoramento de condições de transmissões /." Ilha Solteira, 2019. http://hdl.handle.net/11449/182380.

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Orientador: Aparecido Carlos Gonçalves<br>Resumo: Pares engrenados são elementos de transmissão de potência amplamente utilizados em máquinas e equipamentos, todavia as falhas catastróficas desses componentes são comuns e dispendiosas. A análise de vibrações está entre as técnicas de diagnóstico de defeitos incipientes utilizadas em manutenção preditiva, posto que a presença de uma falha altera o comportamento dinâmico do sistema e o estado de degradação pode ser detectado pelo monitoramento dos sinais de vibração. Na indústria atual, onde as aquisições de dados, tanto para controle de process
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Frini, Marouane. "Diagnostic des engrenages à base des indicateurs géométriques des signaux électriques triphasés." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSES052.

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Bien qu’ils soient largement utilisés dans le domaine, les mesures vibratoires classiques présentent plusieurs limites. A la base, l’analyse vibratoire ne peut identifier qu’environ 60% des défauts qui peuvent survenir dans les machines. Cependant, les principaux inconvénients des mesures de la vibration sont l’accès difficile au système de transmission afin d’y placer le capteur ainsi que le coût conséquent de la mise en œuvre. Ceci résulte en des problèmes de sensibilité relatifs à la position de l’installation et ceux de difficulté pour distinguer la source de vibration à cause de la divers
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Chang, Chih-Chie, and 張智傑. "Gear Fault Diagnosis by Using Fuzzy Neural Network." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/z49un2.

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碩士<br>中原大學<br>機械工程研究所<br>93<br>This paper applies fuzzy neural network (FNN) in the fault diagnosis of the gear-rotor system. According to the document and experiment data, the relationship between fault and frequency spectrum is built up as the rule of approximate reasoning diagnosis and the training data of NN. In this paper, gears which have the four typical faults will be taking into vibrating examining : (1)Gear skew (2)Shaft not parallel (3) Tooth breakage (4)wear. Picking up the characteristic signals by using the technique of analysing spectrums . After classifing by membership functio
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Syu, Jing-Rong, and 許景榮. "Neural Network Application in the Gear Fault Diagnosis." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/74cy3q.

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碩士<br>國立虎尾科技大學<br>機械與機電工程研究所<br>100<br>Due to the variable types of gear faults, the main purpose of this study is to establish a model prototype of diagnostic. Firstly, apply FFT (Fast Fourier Transform), power spectrum, EMD (Empirical Mode Decomposition), and wavelet analysis for signal processing. Then extract the fault characteristic parameters and regularize. BPN (Back-propagation Network) and PNN (Probabilistic Neural Network) are then combined to develope the diagnosis system. This study consists of simulations and examples validation. The first part of the fault types are generated by
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Hung, Ruei-Shiang, and 洪瑞祥. "Fault Diagnosis for Gear Train by Using Unsteady Vibration Analysis." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/8zf335.

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碩士<br>中原大學<br>機械工程研究所<br>98<br>This paper aim to analyze variable speed signal of planetary gear train speed reducer. At first using wavelet transform decomposed vibration signal into low-frequency tread signal and high-frequency detail signal. Then using the Hilbert transform obtained instantaneous frequency ,obtained the instantaneous frequency for the rotation axis by the vibration signal, substitution traditional order tracking needs pulse signal calculate the instantaneous frequency of the tachometer , then carries on resampled in angle-domain to each unsteady vibration signal converted
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Lin, Tung-Liang, and 林東良. "Applying Fuzzy Expert System To Fault Diagnosis In Aircraft Landing-Gear System." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/65873782053959905861.

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碩士<br>義守大學<br>工業工程與管理學系碩士班<br>94<br>While executing the first and second phases of the Troop-Reduction Program, the Refining Program and the Government Owned, Contractor Operated (GOCO) Maintenance Project, the Air Force simultaneously faces the difficulties of organization reconstruction, downsizing logistic manpower, the frequent job changes, and the newly-recruited technicians’ insufficient maintenance capabilities. To successfully fulfill the maintenance missions, it is necessary to recruit the personnel with required knowledge, discipline, and professional skills to provide a safer and mo
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Chen, Jen-chieh, and 陳仁傑. "Application of Hilbert-Huang Transform to Gear Fault Diagnosis under Variable Speed." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/64030137684375061665.

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碩士<br>國立中央大學<br>機械工程研究所<br>99<br>The main purpose of this paper is to study the fault features of gear system, such as gear wearing, teeth broken, gear unbalance, under variable rotation speed. The Hilbert-Huang Transform (HHT) method is utilized to analyze the nonlinear and non-stationary vibration signals. The signals are decomposed into a number of Intrinsic Mode Function (IMF) through the Ensemble Empirical Mode Decomposition (EEMD) and the Post-Processing of EEMD. The three different methods of calculating the instantaneous frequencies, Normalized Hilbert Transform (NHT) method, Generaliz
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Chen, Jian-Ji, and 陳建吉. "Development of a gear-set fault diagnosis system based on sound emission and vibration signals." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/90165558199946052580.

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碩士<br>國立彰化師範大學<br>車輛科技研究所<br>95<br>In this study, a condition monitoring and faults identification technique for rotating machineries using wavelet transform and artificial neural network is presented. In the past, most of the conventional techniques for condition monitoring and fault diagnosis in rotating machinery are based chiefly on analyzing the difference of vibration signal amplitude in the time domain or frequency spectrum. One can not figure out the broken point of the transmission before dismounting the equipment. Therefore, this experimental gear-set platform is simulated as the t
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Books on the topic "Gear fault diagnosis"

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Diagnosis of helicopter gearboxes using structure-based networks. National Aeronautics and Space Administration, 1995.

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Kouresh, Danai, Lewicki David G, and United States. National Aeronautics and Space Administration., eds. Unsupervised pattern classifier for abnormality-scaling of vibration features for helicopter gearbox fault diagnosis. National Aeronautics and Space Administration, 1996.

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Center, Lewis Research, ed. A review of transmission diagnostics research at NASA Lewis Research Center. The Center, 1994.

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Improving the performance of the structure-based connectionist network for diagnosis of helicopter gearboxes. National Aeronautics and Space Administration, 1996.

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Book chapters on the topic "Gear fault diagnosis"

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Penter, Alan. "Practical Gear Fault Diagnosis Using Vibration-based Methods." In COMADEM 89 International. Springer US, 1989. http://dx.doi.org/10.1007/978-1-4684-8905-7_11.

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Zhu, Dashuai, Lizheng Pan, Shigang She, Xianchuan Shi, and Suolin Duan. "Gear Fault Diagnosis Method Based on Feature Fusion and SVM." In Advanced Manufacturing and Automation VIII. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2375-1_10.

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Fang, Xiaoyu, Jianfeng Qu, Yi Chai, Ting Zhong, and Xinhua Yan. "Diagnosis of Intermittent Fault for Gear System Using WPT-GHMM." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00214-5_22.

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Li, Weiming, Yuanfang Chen, and Muhammad Alam. "Distributed Monitoring Architecture for Industrial Safety Based on Gear Fault Diagnosis." In Recent Trends and Advances in Wireless and IoT-enabled Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-99966-1_22.

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Zhang, Lun, and Niaoqing Hu. "Fault Diagnosis of Sun Gear in Planetary Gearbox: A Comparative Study." In Advances in Asset Management and Condition Monitoring. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57745-2_57.

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Yibo, Cao, Xie Xiaopeng, Liu Yan, and Ding Tianhuai. "Fault Diagnosis of Gear Using Oil Monitoring Samples and Vibration Data." In Advanced Tribology. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03653-8_320.

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Song, Mengmeng, and Shungen Xiao. "A Fault Diagnosis Method of Gear Based on SVD and Improved EEMD." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6373-2_7.

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Shen, Ping-chen, Yuan Kang, Chun-chieh Wang, Yeon-pun Chang, and Hsing-han Lee. "Study on the Affection of Gear Fault Diagnosis Bases on HHT by Noises." In Advances in Soft Computing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03664-4_10.

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Lu, Yong, Yourong Li, Han Xiao, and Zhigang Wang. "A Sliding Singular Spectrum Entropy Method and Its Application to Gear Fault Diagnosis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87442-3_83.

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Sharma, Vikas, and Anand Parey. "Use of Cyclostationarity Based Condition Indicators for Gear Fault Diagnosis Under Fluctuating Speed Condition." In Applied Condition Monitoring. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51445-1_15.

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Conference papers on the topic "Gear fault diagnosis"

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Özgüven, H. Nevzat, and Melek Yalçintaş. "Effect of Operating Speed in Diagnosis of Gear Faults." In ASME 1991 Design Technical Conferences. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/detc1991-0380.

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Abstract This paper presents the effect of the operational speed of gears on the vibration signals measured at bearings for gear fault diagnosis purposes.The information available in literature is usually for relatively low speeds of gears. As it is very difficult to study experimentally the vibration of high speed gears with controlled specific gear faults, computer simulation is used in this work. Nonlinear dynamic analysis program developed in an earlier study is used in simulating the dynamic behavior of spur gears with several different gear faults, and an FFT algorithm is used to generat
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Li, He, Yimin Zhang, Bangchun Wen, and Feng Wen. "Gear Wearing Faults Diagnosis Using Order Tracking." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35119.

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Machine vibration signal has been used in fault detection and diagnosis. Modulation and non-stationary existing in the signal generated by a faulty gearbox presents challenges to effective fault detection. In order to study the phenomenon of gear wearing, the vibration of a gearbox in a transmission shaft is measured by the order tracking method. Order tracking has the ability to detect the fault issue of gear wearing. Measured results indicate characteristic parameters of gear wearing. And compared with the tradition spectral analysis, the advantages of order tracking were presented in the pa
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Ma, Shang-Jun, Geng Liu, and Yongqiang Xu. "Gear fault diagnosis based on SVM." In 2010 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR 2010). IEEE, 2010. http://dx.doi.org/10.1109/icwapr.2010.5576299.

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Sun, Pan, Yimin Shao, Xiaoxi Ding, and Minggang Du. "Gearbox Fault Diagnosis Using Multiscale Sparse Spectrum." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85889.

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Due to its special modulation mechanism with multiple units (eg. shafts, gears, etc.) under various conditions, the related fault information of gear fault would distribute in a broad frequency band. In this manner, it is not easy about accurately detecting the early-stage gear fault by detecting the fault frequency in a limited frequency band. In this paper, a new spectral analysis, called multiscale sparse spectrum (MSS), is proposed to achieve fault frequency detection in a sound way. The overall frequency information about the raw signal is firstly sensed by a series of frequency-window fu
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Hajnayeb, Ali, Ahmad Ghasemloonia, Siamak Esmaeelzadeh Khadem, and Mohammad Hasan Moradi. "Improving Performance of an Artificial Neural Network Based Gearbox Fault Diagnosis System." In ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2010. http://dx.doi.org/10.1115/esda2010-25087.

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The automatic vibration monitoring methods of gears and gearboxes due to their extensive applications in industry are improving. Hence, their vibration signal and its derived features, has been an interesting topic for researchers in this field. On the other hand, optimizing the number of vibration signal features used in the detection and diagnosis process is crucial for increasing the fault detection speed of automatic condition monitoring systems. In this paper, a system based on multiple layer perceptron artificial neural networks (MLP ANNs) is designed to diagnose different types of fault
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Meng, Jing, Liye Zhao, and Ruqiang Yan. "Gear Fault Diagnosis Based on Recurrence Network." In 2017 International Conference on Sensing, Diagnostics, Prognostics and Control (SDPC). IEEE, 2017. http://dx.doi.org/10.1109/sdpc.2017.103.

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Zhao, Hao, Weifei Hu, Zhenyu Liu, and Jianrong Tan. "A CapsNet-Based Fault Diagnosis Method for a Digital Twin of a Wind Turbine Gearbox." In ASME 2021 Power Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/power2021-66029.

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Abstract Accurate fault diagnosis of complex energy systems, such as wind turbines, is essential to avoid catastrophic accidents and ensure a stable power source. However, accurate fault diagnoses under dynamic operating conditions and various failure mechanisms are major challenges for wind turbines nowadays. Here we present a CapsNet-based deep learning scheme for data-driven fault diagnosis used in a digital twin of a wind turbine gearbox. The CapsNet model can extract the multi-dimensional features and rich spatial information from the gearbox monitoring data by an artificial neural networ
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Yakeu Happi, Kemajou Herbert, Bernard Xavier Tchomeni Kouejou, and Alfayo Anyika Alugongo. "Crack Fault Diagnosis for Spur Gears Using Gear Frequency-RPM spectrum." In 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT). IEEE, 2021. http://dx.doi.org/10.1109/icmimt52186.2021.9476182.

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Karpat, Fatih, Ahmet Emir Dirik, Onur Can Kalay, Oğuz Doğan, and Burak Korcuklu. "Vibration-Based Early Crack Diagnosis With Machine Learning for Spur Gears." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-24006.

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Abstract Gear mechanisms are one of the most significant components of the power transmission systems. Due to increasing emphasis on the high-speed, longer working life, high torques, etc. cracks may be observed on the gear surface. Recently, Machine Learning (ML) algorithms have started to be used frequently in fault diagnosis with developing technology. The aim of this study is to determine the gear root crack and its degree with vibration-based diagnostics approach using ML algorithms. To perform early crack detection, the single tooth stiffness and the mesh stiffness calculated via ANSYS f
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Zhou, Kai, and J. Tang. "Fuzzy Classification of Gear Fault Using Principal Component Analysis-Based Fuzzy Neural Network." In 2020 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/isfa2020-9632.

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Abstract Condition assessment of machinery components such as gears is important to maintain their normal operations and thus can bring benefit to their life circle management. Data-driven approaches haven been a promising way for such gear condition monitoring and fault diagnosis. In practical situation, gears generally have a variety of fault types, some of which exhibit continuous severities of fault. Vibration data collected oftentimes are limited to reflect all possible fault types. Therefore, there is practical need to utilize the data with a few discrete fault severities in training and
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