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Research on Smart Condition Diagnosis System of Production Equipment-Intelligent Vibration Signal Processing Method for Condition Diagnosis of Rotating Machinery

SONG XUEWEI 三重大学

2022.12.08

概要

Rotating machinery is an important and indispensable engineering equipment in industries such as electric power, petrochemical, metallurgy, rail transit and marine ships. Once fault occurs, not only the rotating machinery itself is damaged, so serious that it led to economic losses, major accidents, and life-threatening. With the development of the industrial intelligence, the fault diagnosis of rotating machinery based on vibration signal is becoming more and more extensive application. However, due to the complication of rotary machinery, bad working environment, and variable operating conditions, the vibration signal acquired by the acceleration sensor has the characteristic of non-stationarity, non-linearity, and complexity. At the same time, affected by factors such as transmission loss, signal attenuation, and strong background noise, the regularity fault impact contained in the vibration signal is further weakened. The fault characteristic frequency in the spectrum is more difficult to extract, and it is more difficult to realize the accurate fault diagnosis of rotating machinery. Therefore, the research on effective vibration signal processing method for rotating machinery fault diagnosis has important engineering application significance.

Aiming at the key problems that urgently need to be solved in the signal processing of rotating machinery fault diagnosis, such as suppressing background noise and enhancing fault feature information, the thesis carried out the research about signal fault impact enhancement, signal non-stationarity decomposition, signal adaptive filtering and signal image conversion. By analyzing the rotating machinery vibration signals under different working conditions, the characteristics of the signal are deeply studied, and the corresponding signal processing methods are proposed in a targeted manner. The specific research contents are as follows:

(1) Aiming at the problems of strong background noise and submerged regular impact in vibration signals, a signal processing method based on weighted kurtosis variational modal decomposition (VMD) and improved frequency-weighted energy operator(IFWEO) is proposed. Firstly, the raw signal is decomposed by VMD, and the weighted kurtosis is employed to select the intrinsic mode function (IMF) optimally to reconstruct the signal. The reconstructed signal will carry abundant fault information. Secondly, a third-order cumulant method is introduced to improve the frequency-weighted energy operator (FWEO), which could strengthen the signal impulse and enhance the fault feature. The IFWEO could better effectively reduce the noise impact. Finally, the method is validated in low-speed bearing fault diagnosis.

(2) Aiming at the non-stationary and non-linear of vibration signal, this chapter proposed a signal filtering and fault characteristic enhancement method based on reconstruction adaptive determinate stationary subspace filtering (Rad-SSF) and enhanced third-order spectrum to address the above-mentioned problems. In particular, Rad-SSF reconstructs an adaptive self-determined and decomposed vibration signal trajectory matrix to obtain the non-stationary signals. Thereafter, the filtered signal with the best fault characteristics is extracted according to the kurtosis. Meanwhile, a 1.5-dimensional third-order energy spectrum is performed to enhance the fault characteristics by strengthening the fundamental frequency and eliminating non-coupling harmonics. Finally, the method is validated in high-speed bearing fault diagnosis.

(3) To solve the problem where the actual rotating frequency and its harmonics cannot be accurately extracted in engineering applications, an improved adaptive multi-band filtering method is designed. This method takes the theoretical rotating frequency as the search center, extracts the maximum within the positive and negative deviation as the actual rotating frequency, and sets a threshold according to the actual value to realize multi-band filtering. This method can effectively remove background noise and accurately extract the actual rotating frequency and its harmonics. This model can automatically extract the in-depth features of the filtered signal and improve the fault classification accuracy. Finally, the method is validated in rotating machinery abnormal structure fault diagnosis.

(4) Aiming at the problem that the discrimination between fault categories is not obvious after one-dimensional vibration signal is converted to two-dimensional image, an incrementally accumulated holographic symmetrical dot pattern (SDP) characteristic fusion method is proposed in this chapter. The current study simultaneously extracts the time-and frequency-domain characteristic parameters of vibration signal based on the incremental accumulation method to avoid inconspicuous difference and small discrimination generated by a single parameter. Subsequently, the extracted characteristic signals are transformed into a 2D image based on the SDP method to enhance the differences between signals. Finally, the method is validated in rotating machinery bearing fault diagnosis.

The vibration signal processing methods of rotating machinery proposed in this thesis have been verified by simulation experiments and engineering experiments, and the verification results prove that the proposed methods can realize effective and targeted signal processing.

The main contribution of this thesis is to propose the corresponding signal processing method according to the unique characteristics of vibration signals under different operating conditions and the actual engineering application of rotating machinery fault diagnosis, which effectively suppresses the background noise, enhances the fault characteristic signal, and increases the discrimination between fault types.

参考文献

[1] Lucas C.Brito, Gian Antonio Susto, Jorge N. Brito, et al., “An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery”, Mechanical Systems and Signal Processing, 2022, 163: 108105.

[2] Heng Sun, Min Xia, Yawei Hu, et al., “A new sorting feature-based temporal convolutional network for remaining useful life prediction of rotating machinery”, Computers & Electrical Engineering, 2021, 95: 107413.

[3] Hao Wei, Qinghua Zhang, Minghu Shang, et al., “Extreme learning Machine-based classifier for fault diagnosis of rotating Machinery using a residual network and continuous wavelet transform”, Measurement, 2021, 183: 109864.

[4] Jing Yuan, Ze Yao, Huiming Jiang, et al., “Multi-lifting synchrosqueezing transform for nonstationary signal analysis of rotating machinery”, Measurement, 2022, 191: 110758.

[5] Jungho Park, Yunhan Kim, Kyumin Na, et al., “An image-based feature extraction method for fault diagnosis of variable-speed rotating machinery”, Mechanical Systems and Signal Processing, 2022, 167: 108524.

[6] Yongbo Li, Shun Wang, Yang Yang, et al., “Multiscale symbolic fuzzy entropy: An entropy denoising method for weak feature extraction of rotating machinery”, Mechanical Systems and Signal Processing, 2022, 162: 108052.

[7] Na Lu, Mingliang Li, Guangtao Zhang, et al., “Fault feature extraction method for rotating machinery based on a CEEMDAN-LPP algorithm and synthetic maximum index”, Measurement, 2022, 189: 110636.

[8] Zhiqiang Zhang, and Qingyu Yang, “Unsupervised feature learning with reconstruction sparse filtering for intelligent fault diagnosis of rotating machinery”, Applied Soft Computing, 2022, 115: 108207.

[9] Guoqiang Li, Jun Wu, Chao Deng, et al., “Parallel multi-fusion convolutional neural networks based fault diagnosis of rotating machinery under noisy environments”, ISA Transactions, 2021, online.

[10] Wei Li, Xiang Zhong, Haidong Shao, et al., “Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework”, Advanced Engineering Informatics, 2022, 52: 101552.

[11] Fuming Zhou, Jun Han, and Xiaoqiang Yang, “Multivariate hierarchical multiscale fluctuation dispersion entropy: Applications to fault diagnosis of rotating machinery”, Applied Acoustics, 2021, 182(1-2): 108271.

[12] Pengfei Liang, Chao Deng, Jun Wu, et al., “Intelligent fault diagnosis of rotating machinery via wavelet transform, generative adversarial nets and convolutional neural network”, Measurement, 2020, 159: 107768.

[13] Jingli Yang, Yongqi Chang, Tianyu Gao, et al., “Failure prediction of the rotating machinery based on ceemdan-apen feature and ar-ukf model”, Applied Sciences, 2020, 10(6): 2056.

[14] Yongbo Li, Xianzhi Wang, Shubin Si, et al., “A new intelligent fault diagnosis method of rotating machinery under varying-speed conditions using infrared thermography”, Complexity, 2019, 2019(5): 1-12.

[15] Yiwei Cheng, Manxi Lin, Jun Wu, et al., “Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network”, Knowledge-Based Systems, 2021, 216: 106796.

[16] Shaomin Zhu, Hong Xia, Binsen Peng, et al., “Yingying Jiang, Feature extraction for early fault detection in rotating machinery of nuclear power plants based on adaptive VMD and Teager energy operator”, Annals of Nuclear Energy, 2021, 160: 108392.

[17] Yibing Li, Weiteng Zou, and Li Jiang, “Fault diagnosis of rotating machinery based on combination of Wasserstein generative adversarial networks and long short-term memory fully convolutional network”, Measurement, 2022, 191: 110826.

[18] Dong Zhang, and Zhipeng Feng, “Enhancement of time-frequency post-processing readability for nonstationary signal analysis of rotating machinery: Principle and validation”, Mechanical Systems and Signal Processing, 2022, 163: 108145.

[19] Kaixuan Liang, Ming Zhao, Jing Lin, et al., “Maximum average kurtosis deconvolution and its application for the impulsive fault feature enhancement of rotating machinery”, Mechanical Systems and Signal Processing, 2021, 149: 107323.

[20] Yongxing Song, Jingting Liu, Ning Chu, et al., “A novel demodulation method for rotating machinery based on time-frequency analysis and principal component analysis”, Journal of Sound and Vibration, 2019, 442: 645-656.

[21] Wei Li, Zhencai Zhu, Fan Jiang, et al. “Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method”, Mechanical Systems and Signal Processing, 2015, 50 - 51: 414 - 426.

[22] Chuan Li, Diego Cabrera, José Valente de Oliveira, et al., “Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram”, Mechanical Systems and Signal Processing, 2016, 76: 157 - 173.

[23] Lei Wang, Zhiwen Liu, Qiang Miao, et al., “Time-frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis”, Mechanical Systems and Signal Processing, 2018, 103: 60 - 75.

[24] Liuyang Song, Huaqing Wang, and Peng Chen, “Vibration-based intelligent fault diagnosis for roller bearings in low-speed rotating machinery”, IEEE Transactions on Instrumentation and Measurement, 2018, 67(8): 1887 - 1899.

[25] Myungyon Kim, Jin Uk Ko, Jinwook Lee, et al., “A domain adaptation with semantic clustering (dasc) method for fault diagnosis of rotating machinery”, ISA Transactions, 2022, 120: 372 - 382.

[26] Jinwook Lee, Myungyon Kim, Jin Uk Ko, et al., “Asymmetric inter-intra domain alignments (AIIDA) method for intelligent fault diagnosis of rotating machinery”, Reliability Engineering & System Safety, 2022, 218: 108186.

[27] Yuanhong Chang, Jinglong Chen, Haixin Lv, et al., “Heterogeneous bi-directional recurrent neural network combining fusion health indicator for predictive analytics of rotating machinery”, ISA Transactions, 2021, 122: 409 - 423.

[28] Yaowei Shi, Aidong Deng, Xue Ding, et al., “Multisource domain factorization network for crossdomain fault diagnosis of rotating machinery: An unsupervised multisource domain adaptation method”, Mechanical Systems and Signal Processing, 2022, 164: 108219.

[29] Shangjun Ma, Wei Cai, Kaiwen Liu, et al., “A lighted deep convolutional neural network-based fault diagnosis of rotating machinery”, Sensors, 2019, 19(10): 2381.

[30] Ruonan Liu, Boyuan Yang, Enrico Zio, et al., “Artificial intelligence for fault diagnosis of rotating machinery: A review”, Mechanical Systems and Signal Processing, 2018, 108: 33 - 47.

[31] Feng Jia, Lei Yaguo, Jing Lin, et al., “Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data”, Mechanical Systems and Signal Processing, 2016, 72-73: 303 - 315.

[32] Min Xia, Teng Li, Lin Xu, et al., “Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks”, IEEE/ASME Transactions on Mechatronics, 2017, 23(1): 101 - 110.

[33] Guangyao Zhang, Yi Wang, Xiaomeng Li, et al., “Enhanced symplectic geometry mode decomposition and its application to rotating machinery fault diagnosis under variable speed conditions”, Mechanical Systems and Signal Processing, 2022, 170: 108841.

[34] Yanli Ma, Junsheng Cheng, Ping Wang, et al., “Rotating machinery fault diagnosis based on multivariate multiscale fuzzy distribution entropy and Fisher score”, Measurement, 2021,179: 109495.

[35] Xiaowang Chen, and Zhipeng Feng, “Order spectrum analysis enhanced by surrogate test and VoldKalman filtering for rotating machinery fault diagnosis under time-varying speed conditions”, Mechanical Systems and Signal Processing, 2021, 154: 107585.

[36] Yahui Zhang, Taotao Zhou, Xufeng Huang, et al., “Fault diagnosis of rotating machinery based on recurrent neural networks”, Measurement, 2021, 171: 108774.

[37] Xiaoli Zhao, and Minping Jia, “A new Local-Global Deep Neural Network and its application in rotating machinery fault diagnosis”, Neurocomputing, 2019, 366: 215 - 233.

[38] Lei Wang, Zhiwen Liu, Hongrui Cao, et al., “Subband averaging kurtogram with dual-tree complex wavelet packet transform for rotating machinery fault diagnosis”, Mechanical Systems and Signal Processing, 2020, 142: 106755.

[39] Lei Xiao, Junxuan Tang, Xinghui Zhang, et al., “Weak fault detection in rotating machineries by using vibrational resonance and coupled varying-stable non-linear systems”, Journal of Sound and Vibration, 2020, 478: 115355.

[40] Peng Zhou, Yang Yang, Hong Wang, et al., “The relationship between fault-induced impulses and harmonic-cluster with applications to rotating machinery fault diagnosis”, Mechanical Systems and Signal Processing, 2020, 144: 106896.

[41] Brandon Van Hecke, Jae Yoon, and David He, “Low speed bearing fault diagnosis using acoustic emission sensors,” Applied Acoustics, 2016, 105: 35 - 44.

[42] Henry Omoregbee, and Stephan Heyns, “Fault Classification of Low-Speed Bearings Based on Support Vector Machine for Regression and Genetic Algorithms Using Acoustic Emission,” Journal of Vibration Engineering and Technologies, 2019, 7(5): 455 - 464.

[43] Wael Moustafa, Cousinard Olivier, Fabrice Bolaers, et al., “Low speed bearings fault detection and size estimation using instantaneous angular speed,” Journal of Vibration and Control, 2016, 22(15): 3413 - 3425.

[44] Lili Bai, Zhennan Han, Jiajun Ren, et al., “Research on feature selection for rotating machinery based on supervision kernel entropy component analysis with whale optimization algorithm”, Applied Soft Computing, 2020, 92: 106245.

[45] A Khadersab, and S Shivakumar, “Vibration Analysis Techniques for Rotating Machinery and its effect on Bearing Faults”, Procedia Manufacturing, 2018, 20: 247 - 252.

[46] Xunshi Yan, Zhe Sun, Jingjing Zhao, et al., “Fault diagnosis of rotating machinery equipped with multiple sensors using space-time fragments”, Journal of Sound and Vibration, 2019, 456: 49 - 64.

[47] Fafa Chen, Baoping Tang, Tao Song, et al., “Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization”, Measurement, 2014, 47: 576 - 90.

[48] Zhiwei Cheng, and Bin Cai, “Predicting the remaining useful life of rolling element bearings using locally linear fusion regression”, Journal of Intelligent and Fuzzy Systems, 2018, 34(6): 1 - 12.

[49] Zehui Hua, Juanjuan Shi, Yang Luo, et al., “Iterative matching synchrosqueezing transform and application to rotating machinery fault diagnosis under nonstationary conditions”, Measurement, 2021, 173: 108592.

[50] Masoud Jalayer, Carlotta Orsenigo, and Carlo Vercellis, “Fault detection and diagnosis for rotating machinery: A model based on convolutional LSTM, Fast Fourier and continuous wavelet transforms”, Computers in Industry, 2021, 125: 103378.

[51] Zhiliang Liu, Yaqiang Jin, Ming J. Zuo, et al., “ACCUGRAM: A novel approach based on classification to frequency band selection for rotating machinery fault diagnosis”, ISA Transactions, 2019, 95: 346 - 357.

[52] Jinde Zheng, Haiyang Pan, Shubao Yuan, et al., “Adaptive parameterless empirical wavelet transform based time-frequency analysis method and its application to rotor rubbing fault diagnosis”, Signal Processing, 2017, 130: 305 - 314.

[53] Ying Zhang, Kangshuo Xing, Ruxue Bai, et al., “An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image”, Measurement, 2020, 157: 107667.

[54] Shan Pang, Xinyi Yang, Xiaofeng Zhang, et al., “Fault diagnosis of rotating machinery with ensemble kernel extreme learning machine based on fused multi-domain features”, ISA Transactions, 2020, 98: 320 - 337.

[55] Ali Dibaj, Mir Mohammad Ettefagh, Reza Hassannejad, et al., “A hybrid fine-tuned VMD and CNN scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults”, Expert Systems with Applications, 2021, 167: 114094.

[56] Zhiqiang Liao, Xuewei Song, Baozhu Jia, et al., “Bearing Fault Feature Enhancement and Diagnosis Based on Statistical Filtering and 1.5-Dimensional Symmetric Difference Analytic Energy Spectrum,” IEEE Sensors Journal, 2021, 21(8): 9959 - 9968.

[57] Huaiqian Bao, Zhenhao Yan, Shanshan Ji, et al., “An enhanced sparse filtering method for transfer fault diagnosis using maximum classifier discrepancy,” Measurement Science and Technology, 2021, 32(8): 1 - 12.

[58] Moise Avoci Ugwiri, Marco Carratú, Vincenzo Paciello, et al., “Benefits of enhanced techniques combining negentropy, spectral correlation and kurtogram for bearing fault diagnosis”, Measurement, 2021, 185: 110013.

[59] Xuefang Xu, Zijian Qiao, and Yaguo Lei, “Repetitive transient extraction for machinery fault diagnosis using multiscale fractional order entropy infogram,” Mechanical Systems and Signal Processing, 2018, 103: 312 - 326.

[60] Yusuke Kobayashi, Liuyang Song, Masaru Tomita, et al., “Automatic Fault Detection and Isolation Method for Roller Bearing Using Hybrid-GA and Sequential Fuzzy Inference,” Sensors, 2019, 19(16): 3553.

[61] Xiaohui Gu, Shaopu Yang, Yongqiang Liu, et al., “Rolling element bearing faults diagnosis based on kurtogram and frequency domain correlated kurtosis,” Measurement Science and Technology, 2016, 27(12): 125019.

[62] Kaixuan Liang, Ming Zhao, Jing Lin, et al., “A Novel Indicator to Improve Fast Kurtogram for the Health Monitoring of Rolling Bearing,” IEEE Sensors Journal, 2020, 20(20): 12252 - 12261.

[63] Andre A.Silva, ShalabhGupta, Ali M.Bazzi, et al., “Waveletbased information filtering for fault diagnosis of electric drive systems in electric ships,” 2018, ISA Transactions, 78: 105 - 115.

[64] Kamel Belaid, Abdelhamid Miloudi, and Hadjila Bournine, “The processing of resonances excited by gear faults using continuous wavelet transform with adaptive complex morlet wavelet and sparsity measurement,” Measurement, 2021, 180(5): 109576.

[65] Qing Xiong, Yanhai Xu, Yiqiang Peng, et al., “Lowspeed rolling bearing fault diagnosis based on EMD denoising and parameter estimate with alpha stable distribution,” Journal of Mechanical Science and Technology, 2017, 31(4): 1587 - 1601.

[66] Ke Zhang, Tianran Lin, and Xia Jin, “Low speed bearing fault diagnosis based on EMD-CIIT histogram entropy and KFCM clustering,” Journal of Shanghai Jiaotong University (Science), 2019, 24(5): 616 - 621.

[67] Liye Zhao, Wei Yu, and Ruqiang Yan, “Rolling bearing fault diagnosis based on ceemd and time series modeling”, Mathematical Problems in Engineering, 2014, 2014(2014): 1 - 13.

[68] Xuerong Ye, Yifan Hu, Juxian Shen, et al., “An adaptive optimized TVF-EMD based on a sparsityimpact measure index for bearing incipient fault diagnosis”, IEEE Transactions on Instrumentation and Measurement, 70: 1 - 10.

[69] Yongjian Sun, Shaohui Li, Yaling Wang, et al., “Fault diagnosis of rolling bearing based on empirical mode decomposition and improved manhattan distance in symmetrized dot pattern image,” Mechanical Systems and Signal Processing, 2021, 159: 107817.

[70] Fuzheng Liu, Junwei Gao, and Liu Huabo, “The feature extraction and diagnosis of rolling bearing based on CEEMD and LDWPSO-PNN”, IEEE Access, 2020, 8: 19810 - 19819.

[71] Shuzhi Gao, Quan Wang, Yimin Zhang, “Rolling bearing fault diagnosis based on ceemdan and refined composite multi-scale fuzzy entropy”, IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1 - 10.

[72] Lida Liao, Bin Huang, Qi Tan, et al., “Development of an Improved LMD Method for the LowFrequency Elements Extraction from Turbine Noise Background,” Energies, 2020, 13(4): 1 - 17.

[73] Yu Zhang, Zhuoyou Fan, Xiaorong Gao, et al., “A Fault Diagnosis Method of Train Wheelset Rolling Bearing Combined with Improved LMD and FK,” Journal of Sensors, 2019, 2019(18): 1 - 11.

[74] Lei Wang, Zhiwen Liu, Qiang Miao, et al., “Time–frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis,” Mechanical Systems and Signal Processing, 2018, 103: 60 - 75.

[75] Jianbo Yu, and Jingxiang Lv, “Weak fault feature extraction of rolling bearings using local mean decomposition-based multilayer hybrid denoising”, IEEE Transactions on Instrumentation and Measurement, 2017, 66(12): 3148 - 3159.

[76] Jinbao Zhang, Yongqiang Zhao, Xinglin Li, et al., “Bearing fault diagnosis with kernel sparse representation classification based on adaptive local iterative filtering-enhanced multiscale entropy features”, Mathematical Problems in Engineering, 2019, 2019(17): 1 - 17.

[77] Lei Zhao, Yongxiang Zhang, and Danchen Zhu, “Rolling Element Bearing Fault Diagnosis Based on Adaptive Local Iterative Filtering Decomposition and Teager–Kaiser Energy Operator”, Journal of Failure Analysis and Prevention, 2019, 2019(4): 1018 - 1022.

[78] Mingyue Yu, and Xiang Pan, “A novel ITD-GSP-based characteristic extraction method for compound faults of rolling bearing,” Measurement, 2020, 159: 107736.

[79] Jiansong Zhou, Ka-kit Tung, and King-Fa Li, “Multi-decadal variability in the Greenland ice core records obtained using intrinsic timescale decomposition,” Climate Dynamics, 2016, 47(3 - 4): 739 - 752.

[80] Jianbo Yu, and Haiqiang Liu, “Sparse coding shrinkage in intrinsic time-scale decomposition for weak fault feature extraction of bearings”, IEEE Transactions on Instrumentation and Measurement, 2018, 67(7): 1579 - 1592.

[81] Ying Zhang, Chao Zhang, Xinyuan Liu, et al., “Fault diagnosis method of wind turbine bearing based on improved intrinsic time-scale decomposition and spectral kurtosis”, 2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI). IEEE, 2019.

[82] Jiakai Ding, Liangpei Huang, Dongming Xiao, et al., “GMPSOVMD algorithm and its application to rolling bearing fault feature extraction,” Sensors, 2020, 20(7): 1946 - 1969.

[83] Xin Li, Zengqiang Ma, De Kang, et al., “Fault diagnosis for rolling bearing based on VMD-FRFT,” Measurement, 2020, 155: 1 - 17.

[84] Zipeng Li, Jinglong Chen, Yanyang Zi, et al., “Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive,” Mechanical Systems and Signal Processing, 2017, 85: 512 - 529.

[85] Ali Dibaj, Reza Hassannejad, Mir Mohammad Ettefagh, et al., “Incipient fault diagnosis of bearings based on parameter-optimized VMD and envelope spectrum weighted kurtosis index with a new sensitivity assessment threshold,” ISA Transactions, 2021, 114: 413 - 433.

[86] Gang Yao, Yunce Wang, Mohamed Benbouzid, et al, “A Hybrid Gearbox Fault Diagnosis Method Based on GWO-VMD and DE-KELM,” Applied Sciences, 2021, 11(4996): 1 - 29.

[87] Xinglong Pei, Xiaoyang Zheng, and Jinliang Wu, “Intelligent bearing fault diagnosis based on Teager energy operator demodulation and multiscale compressed sensing deep autoencoder”, Measurement, 2021, 179, 109452.

[88] Maosen Cao, Wei Xu, Wieslaw Ostachowicz, et al, “Damage identification for beams in noisy conditions based on Teager energy operator-wavelet transform modal curvature”, Journal of Sound and Vibration, 2014, 333(6): 1543 - 1553.

[89] Ming Liang, and I. Soltani Bozchalooi, “An energy operator approach to joint application of amplitude and frequency-demodulations for bearing fault detection”, Mechanical Systems and Signal Processing, 2010, 24(5): 1473 - 1494.

[90] A. Gałęzia, and K. Gryllias, “Application of the combined Teager-Kaiser envelope for bearing fault diagnosis”, Measurement, 2021, 182: 109710.

[91] Tian Han, Qiannan Liu, Li Zhang, et al., “Fault feature extraction of low speed roller bearing based on Teager energy operator and CEEMD,” Measurement, 2019, 138: 400 - 408.

[92] John M. O'Toole, Andriy Temko, and Nathan Stevenson, “Assessing instantaneous energy in the EEG: a non-negative, frequencyweighted energy operator”, In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Societypp, 2014, 3288 - 3291.

[93] Yuanbo Xu, Fan Fan, and Xiangkui Jiang, “A fast iterative filtering decomposition and symmetric difference analytic energy operator for bearing fault extraction”, ISA Transactions, 2021, 108: 317 - 332.

[94] H. Faghidi, and Ming Liang, “Bearing fault identification by higher order energy operator fusion: A non-resonance based approach”, Journal of Sound and Vibration, 2016, 381: 83 - 100.

[95] Yacine Imaouchen, Mourad Kedadouche, and Rezak Alkama, “A frequency-weighted energy operator and complementary ensemble empirical mode decomposition for bearing fault detection”, Mechanical Systems and Signal Processing, 2017, 82: 103 - 116.

[96] Xiaoxi Ding, Quanchang Li, Lun Lin, et al., “Fast time-frequency manifold learning and its reconstruction for transient feature extraction in rotating machinery fault diagnosis”, Measurement, 2019, 141: 380 - 395.

[97] Chang Liu, “A tacholess order tracking method based on inverse short time Fourier transform and singular value decomposition for bearing fault diagnosis”, Sensors, 2019, 20: 6924.

[98] Zhihao Chen, Jian Cen, Jianbin Xiong, et al., “Rolling bearing fault diagnosis using time-frequency analysis and deep transfer convolutional neural network”, IEEE Access, 8: 150248 - 150261.

[99] Xin Zhang, Zhiwen Liu, Jiaxu Wang, et al., “Time–frequency analysis for bearing fault diagnosis using multiple Q-factor Gabor wavelets”, ISA Transactions, 2018, 82: 225 - 234.

[100] Pravin Singru, Vishnuvardhan Krishnakumar, and Dwarkesh Natarajan, “Bearing failure prediction using Wigner–Ville distribution, modified Poincare mapping and fast Fourier transform”, Journal of Vibroengineering, 2018, 20: 127 - 137.

[101] Hongwei Fan, Sijie Shao, Xuhui Zhang, et al., “Intelligent fault diagnosis of rolling bearing using FCM clustering of EMD-PWVD vibration images”, IEEE Access, 2020, 8: 145194 - 145206.

[102] Attoui Issam, Fergani Nadir, Boutasseta Nadir, et al., “A new time-frequency method for identification and classification of ball bearing faults”, Journal of Sound and Vibration, 2017, 397: 241 - 265.

[103] Miao He, and David He, “Deep learning based approach for bearing fault diagnosis”, IEEE Transactions on Industry Applications, 2017, 53(3): 3057 - 3065.

[104] Yiwei Cheng, Manxi Lin, Jun Wu, et al., “Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network”, Knowledge-Based Systems, 2021, 216(1): 106796.

[105] Hosseini Sadegh, Ahmadi Najafabadi Mehdi, and Akhlaghi Mehdi, “Classification of acoustic emission signals generated from journal bearing at different lubrication conditions based on wavelet analysis in combination with artificial neural network and genetic algorithm”, Tribology International, 2016, 95: 426 - 434.

[106] Israel Ruiz Quinde, Jorge Chuya Sumba, Luis Escajeda Ochoa, et al., “Bearing fault diagnosis based on optimal time-frequency representation method”, IFAC-PapersOnLine, 2019, 52(11): 194 - 199.

[107] Cheng Yang, Zhinong Li, Jin Yuan, et al., “Fractional-order smoothed pseudo wigner-ville distribution and its applications in machinery fault diagnosis”, 2017 Prognostics and System Health Management Conference (PHM-Harbin). IEEE, 2017.

[108] Hui Wang, Jiawen Xu, Ruqiang Yan, et al. “A new intelligent bearing fault diagnosis method using SDP representation and SE-CNN”, IEEE Transactions on Instrumentation and Measurement, 2020, 69: 2377 - 2389.

[109] Yongjian Sun, Shaohui Li, and Xiaohong Wang, “Bearing fault diagnosis based on EMD and improved Chebyshev distance in SDP image”, Measurement, 2021, 176: 109100.

[110] Miyazaki Shuuji, Xuewei Song, Zhiqiang Liao, et al., “Low-speed bearing fault diagnosis based on improved statistical filtering and convolutional neural network”, Measurement Science and Technology, 2021, 32(11): 115009.

[111] Syahril Ramadhan Saufi, Zair Asrar Bin Ahmad, Mohd Salman Leong, et al., “Low-speed bearing fault diagnosis based on ARSSAE model using acoustic emission and vibration signals”, IEEE Access, 2019, 7: 46885 - 46897.

[112] Sharma, Rahul, Sircar Pradip, and Pachori Ram Bilas, “Automated focal EEG signal detection based on third order cumulant function”, Biomedical signal processing and control, 2020, 58:101856.1 - 101856.8.

[113] Xiaoan Yan, Minping Jia, and Ling Xiang, “Compound fault diagnosis of rotating machinery based on OVMD and a 1.5-dimension envelope spectrum”, Measurement Science and Technology, 2016, 27(7): 075002 - 075020.

[114] Erhao Meng, Shengzhi Huang, Qiang Huang, et al., “A hybrid VMD-SVM model for practical streamflow prediction using an innovative input selection framework”, Water Resources Management, 2021, 1 - 17.

[115] Guangyi Chen, Changfeng Yan, Jiadong Meng, et al., “Improved VMD-FRFT based on initial center frequency for early fault diagnosis of rolling element bearing”, Measurement Science and Technology, 2021, 32(11): 115024.

[116] Ramakrishna Thirumuru, and Anil Kumar Vuppala, “Application of non-negative frequencyweighted energy operator for vowel region detection”, International Journal of Speech Technology, 2018, 21(2): 1 - 13.

[117] Xiaojiao Gu, Changzheng Chen, and Hyeong Joon Ahn, “Rolling bearing fault signal extraction based on stochastic resonance-based denoising and VMD”, International Journal of Rotating Machinery, 2017, 2017: 531 - 544.

[118] Yi Qin, Lei Jin, Aibing Zhang, et al., “Rolling bearing fault diagnosis with adaptive harmonic kurtosis and improved bat algorithm”, IEEE Transactions on Instrumentation and Measurement, 2020, 70: 1 - 12.

[119] Dehao Wu, Li Sheng, and Donghua Zhou, “Dynamic stationary subspace analysis for monitoring nonstationary dynamic processes”, Industrial & Engineering Chemistry Research, 2020, 59(47): 20787 - 20797.

[120] Zhiqiang Liao, Liuyang Song, Peng Chen, et al., “An automatic filtering method based on an improved genetic algorithm—with application to rolling bearing fault signal extraction”, IEEE Sensors Journal, 2017, 17(19): 6340 - 6349.

[121] Shilun Zuo, and Zhiqiang Liao, “Bearing fault dominant symptom parameters selection based on canonical discriminant analysis and false nearest neighbor using GA filtering signal”, Mathematical Problems in Engineering, 2020, 8: 1 - 13.

[122] Wanke Yu, Chunhui Zhao, and Biao Huang, “Stationary Subspace Analysis-Based Hierarchical Model for Batch Processes Monitoring”, IEEE Transactions on Control Systems Technology, 2020, 29(1): 444 - 453.

[123] Junhao Chen, and Chunhui Zhao, “Exponential stationary subspace analysis for stationary feature analytics and adaptive nonstationary process monitoring”, IEEE Transactions on Industrial Informatics, 2021, 99(12): 8345 - 8356.

[124] Xinglong Pei, Xiaoyang Zheng, and Jinliang Wu, “Intelligent bearing fault diagnosis based on Teager energy operator demodulation and multiscale compressed sensing deep autoencoder”, Measurement, 2021, 179(6): 109452.

[125] Hongshan Zhao, and Lang Li, “Fault diagnosis of wind turbine bearing based on variational mode decomposition and teager energy operator”, IET Renewable Power Generation, 2016, 11(4): 453 - 460.

[126] Andrew Vigoren, and James M. Zavislan, “Optical sectioning enhancement using higher-order moment signals in random speckle-structured illumination microscopy”, Journal of The Optical Society of America A-optics Image Science and Vision, 2018, 35(3): 474 - 479.

[127] Yifan Li, Ming Jing Zuo, and Yuejian Chen, “An enhanced morphology gradient product filter for bearing fault detection”, Mechanical Systems Signal Processing, 2018, 109: 166 - 184.

[128] Zhaoyi Guan, Peng Chen, Xiao Zhang, et al., “Vibration analysis of shaft misalignment and diagnosis method of structure faults for rotating machinery”, International Journal of Performability Engineering, 2017, 13(4): 337 - 347.

[129] Ke Li, Xiong Meng, Fucai Li, et al., “A novel fault diagnosis algorithm for rotating machinery based on a sparsity and neighborhood preserving deep extreme learning machine”, Neurocomputing, 2019, 350: 261 - 270.

[130] Xin Shu, Xiaodi Zhang, and Fan Xu, “Automatic diagnosis of microgrid networks’ power device faults based on stacked denoising autoencoders and adaptive affinity propagation clustering”, Complexity, 2020, 1 - 24.

[131] Xiaofeng Yuan, Ou Chen, Yalin Wang, et al., “Deep quality related feature extraction for soft sensing modeling: a deep learning approach with hybrid VW-SAE”, Neurocomputing, 2020, 396: 375 - 382.

[132] Wei Gao, Rongjong Wai, and Shiqun Chen, “Novel PV fault diagnoses via SAE and improved multigrained cascade forest with string voltage and currents measures”, IEEE Access, 2020, 8: 133144 - 133160.

[133] Manap Mustafa, Nikolovski Srete, Skamyin Aleksandr, et al., “An analysis of voltage source inverter switches fault classification using short time Fourier transform”, International Journal of Power Electronics and Drive Systems, 2021, 12(4): 2209 - 2220.

[134] Hamid Reza Ahmadi, Navideh Mahdavi, and Mahmoud Bayat, “A novel damage identification method based on short time Fourier transform and a new efficient index”, Structures, 2021, 33(4): 3605 - 3614.

[135] Izat Shahsenov a, Ruslan Malikov a, Peter Cook b, et al., “Prediction of gamma ray data from prestack seismic reflection partial angle stacks using continuous wavelet transform and convolutional neural network approach”, Journal of Applied Geophysics, 2022, 197: 104523.

[136] Tian Han, and Zhiqiang Chao, “Fault diagnosis of rolling bearing with uneven data distribution based on continuous wavelet transform and deep convolution generated adversarial network”, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2021, 43(9): 425.

[137] Yanping Liu, Zheng Ma, Huiqin Jia, et al., “Seismic signal filtering based on pseudo wigner-ville distribution and catte model”, Journal of Physics: Conference Series, 2021, 1894(1): 012058.

[138] Tianji Xu, Bingjie Cheng, Shuangcen Niu, et al., “A microscopic ancient river channel identification method based on maximum entropy principle and Wigner-Ville Distribution and its application”, Petroleum Exploration and Development, 2021, 48(6): 1354 - 1366.

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