A review: data driven-based fault diagnosis and RUL prediction of petroleum machinery and equipment

Figures & data

Figure 1. Five steps of FD and RUL prediction.

Figure 2. The methods of FD and RUL prediction.

Figure 3. The structure diagram of the pumping unit.

Figure 4. Diagrams of the failure of bearings and gears.

Figure 5. Typical PME and their main components.

Figure 6. The decomposition of the EMD algorithm.

Figure 7. The process of FD based on KF.

Figure 8. The structure of ANN.

Figure 9. The illustration of basic SVM.

Figure 10. The structure of ELM.

Figure 11. The structure of autoencoder.

Figure 12. The formation process of stacked autoencoders.

Figure 13. The structure of RBM.

Figure 14. The structure of DBN.

Figure 15. The structure of CNN.

Figure 16. The structure of RNN.

Figure 17. The structure of LSTM.

Table 1. Summary of FD and RUL prediction of PME.

Methods	References
STFT	bearings (H. Li et al., 2019; Y. Zhang et al., 2020); gears (Bian et al., 2019); pumps (W. Zhao et al., 2016)
WT	bearings (Islam & Kim, 2019; Wan & Zhang, 2018); gears (Ahuja et al., 2020; Bajric et al., 2016; R. Chen et al., 2019; Liang et al., 2019; Parey & Singh, 2019; L. Wang, Liu, et al., 2020); pumps (H. Yu et al., 2020; Z. Zheng et al., 2019); others (Kordestani et al., 2018; X. Zhang et al., 2019)
EMD	bearings (Abdelkader et al., 2016, 2018; Cheng et al., 2019; L. Wang & Shao, 2020; Xue et al., 2015); gears (Park et al., 2018); motors (Karatoprak & Seker, 2019); pumps (Alabied et al., 2018; Y. Wang, Zhu, et al., 2020); pipelines (C. Guo et al., 2016; Xu et al., 2020)
VMD	bearings (Z. Li et al., 2018; Miao et al., 2019; X. Zhang et al., 2018); gears (Y. Li, Li, et al., 2018; X. Zhang et al., 2018); motors (Z. Guo et al., 2019); pumps (Y. Chen et al., 2019); pipelines (Diao et al., 2020; Ju et al., 2019); others (Yan, Liu, Zhang, et al., 2020; S. Zhao et al., 2020; D. Zou & Ge, 2019)
KF	bearings (Cui et al., 2020, 2019; Singleton et al., 2015); gears (Z. Feng et al., 2019; Y. Li et al., 2019); motors (Kandukuri et al., 2019; Naseri et al., 2020); pipelines (Moulahi & Hmida, 2020); others (Asl et al., 2019; Naseri et al., 2020)
PF	bearings (C. Chen et al., 2011; Lei, Li, Gontarz, et al., 2016; Lei, Li, & Lin, 2016; Qian & Yan, 2015); gears (C. Chen et al., 2012; L. Yang & Chen, 2019); others (K. Peng et al., 2019; J. Yu, 2015; Zio & Peloni, 2011)
ANNs	bearings (Gunerkar et al., 2019; Sharma et al., 2019); pipelines (Bohorquez et al., 2020; Shaik et al., 2020; Tavakoli et al., 2020); others (Laredo et al., 2019)
ELM	bearings (H. Shao et al., 2018; Z. Wang et al., 2019); gears (Z. Chen et al., 2019; X. Liu et al., 2020; Wei et al., 2016); pumps (Jiang et al., 2019); others (Rinanto et al., 2016; X. Zhao et al., 2020)
SVM	bearings (Gong et al., 2019; Z. Liu et al., 2017; Yao et al., 2018; X. L. Zhang et al., 2015; J. D. Zheng et al., 2017); gears (Han et al., 2019; Z. Liu et al., 2017; D. Yang et al., 2015); motors (Gangsar & Tiwari, 2017; Keskes & Braham, 2015); pipelines (C. Wang, Han, Zhang, et al., 2020; R. Xiao et al., 2019)
DNN	bearings (Z. Chen & Li, 2017; Jia et al., 2016; Lu et al., 2017; Meng et al., 2018; Ren et al., 2018; Tan et al., 2015; J. Wang et al., 2019; Wen et al., 2019; X. Zhao & Jia, 2019); gears (Jia et al., 2016; F. Li, Pang, et al., 2019; Y. Li, Cheng, et al., 2018; G. Liu et al., 2018; J. Wang et al., 2017, 2019; X. Zhao & Jia, 2019);
	motors (W. Sun et al., 2016); pipelines (Zadkarami et al.,2020)
DBN	bearings (Che et al., 2020; J. Li, Li, et al., 2020; Yan, Liu, & Jia, 2020; K. Yu et al., 2020; Zhu et al., 2020); gears (H. Chen et al., 2016; Gai et al., 2020; He et al., 2017; J. Li, Li, et al., 2020; Qin et al., 2019; Yan, Liu, & Jia, 2020); pumps (S. Wang et al., 2018); pipelines (Y. Guo et al., 2019);
	others (J. Huang et al., 2019; Lang et al., 2018; T. Zhang et al., 2019)
CNN	bearings (S. Guo et al., 2020; Wen et al., 2018; B. Yang et al., 2019; Zhu et al., 2019); gears (Grezmaka et al., 2019; T. Li et al., 2020; C. Wu et al., 2019); motors (Ince et al., 2016; S. Shao et al., 2020; F. Wang et al., 2021); pumps (Wen et al., 2018); pipelines (Xie et al., 2020)
RNN	bearings (An et al., 2020; L. Guo et al., 2017; F. Li, Chen, et al., 2019; H. Liu et al., 2018); gears (Y. Peng et al., 2017; Yin et al., 2020; J. Yu et al., 2019); motors (D. Xiao et al., 2019); others (Ye et al., 2020)

Li, H., Zhang, Q., Qin, X., & Sun, Y. (2019). Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neural network. Journal of Vibration and Shock, 37(06), 1208–1215.

 Google Scholar

Zhang, Y., Xing, K., Bai, R., Sun, D., & Meng, Z. (2020). An enhanced convolutional neural network for bearing fault diagnosis based on time-frequency image. Measurement, 157, Article 107667. https://doi.org/https://doi.org/10.1016/j.measurement.2020.107667

 Google Scholar

Bian, J., Liu, X., & Xu, X. (2019). Gearbox fault diagnosis method based on deep convolutional neural network vibration signal image recognition. In 2019 14th IEEE international conference on electronic measurement and instruments (ICEMI) (pp. 456–465) . IEEE.

 Google Scholar

Zhao, W., Wang, Z., Ma, J., & Li, L. (2016). Fault diagnosis of a hydraulic pump based on the CEEMD-STFT time-frequency entropy method and multiclass SVM classifier. Shock and Vibration, 2016. Article ID 2609856. https://doi.org/https://doi.org/10.1155/2016/2609856

 Google Scholar

Islam, M. M. M., & Kim, J. M. (2019). Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural network. Computers in Industry, 106, 142–153. https://doi.org/https://doi.org/10.1016/j.compind.2019.01.008

 Web of Science ®Google Scholar

Wan, S., & Zhang, X. (2018). Teager energy entropy ratio of wavelet packet transform and its application in bearing fault diagnosis. Entropy, 20(5), 388. https://doi.org/https://doi.org/10.3390/e20050388

 Web of Science ®Google Scholar

Ahuja, A. S., Ramteke, D. S., & Parey, A. (2020). Vibration-based fault diagnosis of a bevel and spur gearbox using continuous wavelet transform and adaptive neuro-fuzzy inference system. Soft Computing in Condition Monitoring and Diagnostics of Electrical and Mechanical Systems, 1096, 473–496. https://doi.org/https://doi.org/10.1007/978-981-15-1532-3

 Google Scholar

Bajric, R., Zuber, N., Skrimpas, G. A., & Mijatovic, N. (2016). Feature extraction using discrete wavelet transform for gear fault diagnosis of wind turbine gearbox. Shock and Vibration, 2016, Article ID 6748469. https://doi.org/https://doi.org/10.1155/2016/6748469

 Web of Science ®Google Scholar

Chen, R., Huang, X., Yang, L., Xu, X., Zhang, X., & Zhang, Y. (2019). Intelligent fault diagnosis method of planetary gearboxes based on convolution neural network and discrete wavelet transform. Computers in Industry, 106, 48–59. https://doi.org/https://doi.org/10.1016/j.compind.2018.11.003

 Web of Science ®Google Scholar

Liang, P., Deng, C., Wu, J., Yang, Z., Zhu, J., & Zhang, Z. (2019). Compound fault diagnosis of gearboxes via multi-label convolutional neural network and wavelet transform. Computers in Industry, 113, Article 103132. https://doi.org/https://doi.org/10.1016/j.compind.2019.103132

 Google Scholar

Parey, A., & Singh, A. (2019). Gearbox fault diagnosis using acoustic signals, continuous wavelet transform and adaptive neuro-fuzzy inference system. Applied Acoustics, 147, 133–140. https://doi.org/https://doi.org/10.1016/j.apacoust.2018.10.013

 Web of Science ®Google Scholar

Wang, L., Liu, Z., Cao, H., & Zhang, X. (2020). Subband averaging kurtogram with dual-tree complex wavelet packet transform for rotating machinery fault diagnosis. Mechanical Systems and Signal Processing, 142, Article 106755. https://doi.org/https://doi.org/10.1016/j.ymssp.2020.106755

 Google Scholar

Yu, H., Li, H., & Li, Y. (2020). Vibration signal fusion using improved empirical wavelet transform and variance contribution rate for weak fault detection of hydraulic pumps. ISA Transactions, 107, 385–401. https://doi.org/https://doi.org/10.1016/j.isatra.2020.07.025

 PubMed Web of Science ®Google Scholar

Zheng, Z., Wang, Z., Zhu, Y., Tang, S., & Wang, B. (2019). Feature extraction method for hydraulic pump fault signal based on improved empirical wavelet transform. Processes, 7(11), 824. https://doi.org/https://doi.org/10.3390/pr7110824

 Web of Science ®Google Scholar

Kordestani, M., Samadi, M. F., Saif, M., & Khorasani, K. (2018). A new fault diagnosis of multifunctional spoiler system using integrated artificial neural network and discrete wavelet transform methods. IEEE Sensors Journal, 18(12), 4990–5001. https://doi.org/https://doi.org/10.1109/JSEN.2018.2829345

 Web of Science ®Google Scholar

Zhang, X., Wang, J., Liu, Z., & Wang, J. (2019). Weak feature enhancement in machinery fault diagnosis using empirical wavelet transform and an improved adaptive bistable stochastic resonance. ISA Transactions, 84, 283–295. https://doi.org/https://doi.org/10.1016/j.isatra.2018.09.022

 PubMed Web of Science ®Google Scholar

Abdelkader, R., Derouiche, Z., Kaddour, A., & Zergoug, M. (2016). Rolling bearing faults diagnosis based on empirical mode decomposition: Optimized threshold de-noising method. In 2016 8th International conference on modelling, identification and control (ICMIC) (pp. 186–191). IEEE.

 Google Scholar

Abdelkader, R., Kaddour, A., Bendiabdellah, A., & Derouiche, Z. (2018). Rolling bearing fault diagnosis based on an improved denoising method using the complete ensemble empirical mode decomposition and the optimized thresholding operation. IEEE Sensors Journal, 18(17), 7166–7172. https://doi.org/https://doi.org/10.1109/JSEN.7361

 Web of Science ®Google Scholar

Cheng, Y., Wang, Z., Chen, B., Zhang, W., & Huang, G. (2019). An improved complementary ensemble empirical mode decomposition with adaptive noise and its application to rolling element bearing fault diagnosis. ISA Transactions, 91, 218–234. https://doi.org/https://doi.org/10.1016/j.isatra.2019.01.038

 PubMed Web of Science ®Google Scholar

Wang, L., & Shao, Y. (2020). Fault feature extraction of rotating machinery using a reweighted complete ensemble empirical mode decomposition with adaptive noise and demodulation analysis. Mechanical Systems and Signal Processing, 138, Article 106545. https://doi.org/https://doi.org/10.1016/j.ymssp.2019.106545

 Google Scholar

Xue, X., Zhou, J., Xu, Y., Zhu, W., & Li, C. (2015). An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis. Mechanical Systems and Signal Processing, 62–63, 444–459. https://doi.org/https://doi.org/10.1016/j.ymssp.2015.03.002

 Web of Science ®Google Scholar

Park, S., Kim, S., & Choi, J. (2018). Gear fault diagnosis using transmission error and ensemble empirical mode decomposition. Mechanical Systems and Signal Processing, 108, 262–275. https://doi.org/https://doi.org/10.1016/j.ymssp.2018.02.028

 Web of Science ®Google Scholar

Karatoprak, E., & Seker, S. (2019). An improved empirical mode decomposition method using variable window median filter for early fault detection in electric motors. Mathematical Problems in Engineering, 2019. Article ID 8015295. https://doi.org/https://doi.org/10.1155/2019/8015295

 Google Scholar

Alabied, S., Haba, U., Daraz, A., Gu, F., & Ball, A. D. (2018). Empirical mode decomposition of motor current signatures for centrifugal pump diagnostics. In 2018 24th International conference on automation and computing (ICAC) (pp. 1–6). IEEE.

 Google Scholar

Wang, Y., Zhu, Y., Wang, Q., Yuan, S., Tang, S., & Zheng, Z. (2020). Effective component extraction for hydraulic pump pressure signal based on fast empirical mode decomposition and relative entropy. AIP Advances, 10(7https://doi.org/https://doi.org/10.1063/5.0009771

 Google Scholar

Guo, C., Wen, Y., Li, P., & Wen, J. (2016). Adaptive noise cancellation based on EMD in water-supply pipeline leak detection. Measurement, 79, 188–197. https://doi.org/https://doi.org/10.1016/j.measurement.2015.09.048

 Web of Science ®Google Scholar

Xu, C., Du, S., Gong, P., Li, Z., Chen, G., & Song, G. (2020). An improved method for pipeline leakage localization with a single sensor based on modal acoustic emission and empirical mode decomposition with Hilbert transform. IEEE Sensors Journal, 20(10), 5480–5491. https://doi.org/https://doi.org/10.1109/JSEN.7361

 Web of Science ®Google Scholar

Li, Z., Jiang, Y., Guo, Q., Hu, C., & Peng, Z. (2018). Multi-dimensional variational mode decomposition for bearing-crack detection in wind turbines with large driving-speed variations. Renewable Energy, 116, 55–73. https://doi.org/https://doi.org/10.1016/j.renene.2016.12.013

 Web of Science ®Google Scholar

Miao, Y., Zhao, M., & Lin, J. (2019). Identification of mechanical compound-fault based on the improved parameter-adaptive variational mode decomposition. ISA Transactions, 84, 82–95. https://doi.org/https://doi.org/10.1016/j.isatra.2018.10.008

 PubMed Web of Science ®Google Scholar

Zhang, X., Miao, Q., Zhang, H., & Wang, L. (2018). A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery. Mechanical Systems and Signal Processing, 108, 58–72. https://doi.org/https://doi.org/10.1016/j.ymssp.2017.11.029

 Web of Science ®Google Scholar

Li, Y., Li, G., Wei, Y., Liu, B., & Liang, X. (2018). Health condition identification of planetary gearboxes based on variational mode decomposition and generalized composite multi-scale symbolic dynamic entropy. ISA Transactions, 81, 329–341. https://doi.org/https://doi.org/10.1016/j.isatra.2018.06.001

 PubMed Web of Science ®Google Scholar

Guo, Z., Liu, M., Qin, H., & Li, B. (2019). Mechanical fault diagnosis of a DC motor utilizing united variational mode decomposition, SampEn, and random forest-SPRINT algorithm classifiers. Entropy, 21(5), 470. https://doi.org/https://doi.org/10.3390/e21050470

 Web of Science ®Google Scholar

Chen, Y., Huang, G., & Feng, Z. (2019). Early fault diagnosis of high pressure diaphragm pump check valve based on VMD-HMM. In 2019 IEEE 8th data driven control and learning systems conference (DDCLS) (pp. 808–813). IEEE.

 Google Scholar

Diao, X., Jiang, J., Shen, G., Chi, Z., Wang, Z., Ni, L., Mebarki, A., Bian, H., & Hao, Y. (2020). An improved variational mode decomposition method based on particle swarm optimization for leak detection of liquid pipelines. Mechanical Systems and Signal Processing, 143, Article 106787. https://doi.org/https://doi.org/10.1016/j.ymssp.2020.106787

 Google Scholar

Ju, H., Wang, X., Zhang, T., Zhao, Y., & Ullah, Z. (2019). Defect recognition of buried pipeline based on approximate entropy and variational mode decomposition. Metrology and Measurement Systems, 26(4), 739–755. https://doi.org/https://doi.org/10.24425/mms.2019.129587

 Web of Science ®Google Scholar

Yan, X., Liu, Y., Zhang, W., Jia, M., & Wang, X. (2020). Research on a novel improved adaptive variational mode decomposition method in rotor fault diagnosis. Applied Sciences, 10(5), 1696. https://doi.org/https://doi.org/10.3390/app10051696

 Google Scholar

Zhao, S., Chang, K., Wang, E., Li, B., Wang, K., & Wu, Q. (2020). Fault diagnosis of oil pumping machine retarder based on sound texture-vibration entropy characteristics and gray wolf optimization-support vector machine. Shock and Vibration, 2020. Article ID 2709384. https://doi.org/https://doi.org/10.1155/2020/2709384

 Google Scholar

Zou, D., & Ge, X. (2019). A broken rotor bar fault diagnosis approach based on singular value decomposition and variational mode decomposition. In 2019 IEEE transportation electrification conference and expo, Asia-Pacific (ITEC Asia-Pacific) (pp. 1–6). IEEE.

 Google Scholar

Cui, L., Wang, X., Wang, H., & Ma, J. (2020). Research on remaining useful life prediction of rolling element bearings based on time-varying Kalman filter. IEEE Transactions on Instrumentation and Measurement, 69(6), 2858–2867. https://doi.org/https://doi.org/10.1109/TIM.19

 Web of Science ®Google Scholar

Cui, L., Wang, X., Xu, Y. G., Jiang, H., & Zhou, J. P. (2019). A novel switching unscented Kalman filter method for remaining useful life prediction of rolling bearing. Measurement, 135, 678–684. https://doi.org/https://doi.org/10.1016/j.measurement.2018.12.028

 Web of Science ®Google Scholar

Singleton, R. K., Strangas, E. G., & Aviyente, S. (2015). Extended Kalman filtering for remaining-useful-life estimation of bearings. IEEE Transactions on Industrial Electronics, 62(3), 1781–1790. https://doi.org/https://doi.org/10.1109/TIE.2014.2336616

 Web of Science ®Google Scholar

Feng, Z., Zhu, W., & Zhang, D. (2019). Time-frequency demodulation analysis via vold-Kalman filter for wind turbine planetary gearbox fault diagnosis under nonstationary speeds. Mechanical Systems and Signal Processing, 128, 93–109. https://doi.org/https://doi.org/10.1016/j.ymssp.2019.03.036

 Web of Science ®Google Scholar

Li, Y., Feng, K., Liang, X., & Zuo, M. J. (2019). A fault diagnosis method for planetary gearboxes under non-stationary working conditions using improved vold-Kalman filter and multi-scale sample entropy. Journal of Sound and Vibration, 439, 271–286. https://doi.org/https://doi.org/10.1016/j.jsv.2018.09.054

 Web of Science ®Google Scholar

Kandukuri, S. T., Huynh, V. K., & Robbersmyr, K. G. (2019). Diagnostics of stator winding failures in wind turbine pitch motors using vold-Kalman filter. In IECON 2019 – 45th annual conference of the IEEE industrial electronics society (pp. 5992–5997). IEEE.

 Google Scholar

Naseri, F., Schaltz, E., Lu, K., & Farjah, E. (2020). Real-time open-switch fault diagnosis in automotive permanent magnet synchronous motor drives based on Kalman filter. IET Power Electronics, 13(12), 2450–2460. https://doi.org/https://doi.org/10.1049/pel2.v13.12

 Web of Science ®Google Scholar

Moulahi, M. H., & Hmida, F. B. (2020). Extended Kalman filtering for remaining useful lifetime prediction of a pipeline in a two-tank system. In Diagnosis, fault detection and tolerant control (Vol. 269). Springer.

 Google Scholar

Asl, R. M., Hagh, Y. S., Simani, S., & Handroos, H. (2019). Adaptive square-root unscented Kalman filter: An experimental study of hydraulic actuator state estimation. Mechanical Systems and Signal Processing, 132, 670–691. https://doi.org/https://doi.org/10.1016/j.ymssp.2019.07.021

 Web of Science ®Google Scholar

Chen, C., Zhang, B., Vachtsevanos, G., & Orchard, M. (2011). Machine condition prediction based on adaptive neuro-fuzzy and high-order particle filtering. IEEE Transactions on Industrial Electronics, 58(9), 4353–4364. https://doi.org/https://doi.org/10.1109/TIE.2010.2098369

 Web of Science ®Google Scholar

Lei, Y., Li, N., Gontarz, S., Lin, J., Radkowski, S., & Dybala, J. (2016). A model-based method for remaining useful life prediction of machinery. IEEE Transactions on Reliability, 65(3), 1314–1326. https://doi.org/https://doi.org/10.1109/TR.2016.2570568

 Web of Science ®Google Scholar

Lei, Y., Li, N., & Lin, J. (2016). A new method based on stochastic process models for machine remaining useful life prediction. IEEE Transactions on Instrumentation and Measurement, 65(12), 2671–2684. https://doi.org/https://doi.org/10.1109/TIM.2016.2601004

 Web of Science ®Google Scholar

Qian, Y., & Yan, R. (2015). Remaining useful life prediction of rolling bearings using an enhanced particle filter. IEEE Transactions on Instrumentation and Measurement, 64(10), 2696–2707. https://doi.org/https://doi.org/10.1109/TIM.2015.2427891

 Web of Science ®Google Scholar

Chen, C., Vachtsevanos, G., & Orchard, M. E. (2012). Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach. Mechanical Systems and Signal Processing, 28, 597–607. https://doi.org/https://doi.org/10.1016/j.ymssp.2011.10.009

 Web of Science ®Google Scholar

Yang, L., & Chen, H. (2019). Fault diagnosis of gearbox based on RBF-PF and particle swarm optimization wavelet neural network. Neural Computing and Applications, 31(9), 4463–4478. https://doi.org/https://doi.org/10.1007/s00521-018-3525-y

 Web of Science ®Google Scholar

Peng, K., Jiao, R., Dong, J., & Pi, Y. (2019). A deep belief network based health indicator construction and remaining useful life prediction using improved particle filter. Neurocomputing, 361, 19–28. https://doi.org/https://doi.org/10.1016/j.neucom.2019.07.075

 Web of Science ®Google Scholar

Yu, J. (2015). Machine health prognostics using the Bayesian-inference-based probabilistic indication and high-order particle filtering framework. Journal of Sound and Vibration, 358, 97–110. https://doi.org/https://doi.org/10.1016/j.jsv.2015.08.013

 Web of Science ®Google Scholar

Zio, E., & Peloni, G. (2011). Particle filtering prognostic estimation of the remaining useful life of nonlinear components. Reliability Engineering and System Safety, 96(3), 403–409. https://doi.org/https://doi.org/10.1016/j.ress.2010.08.009

 Web of Science ®Google Scholar

Gunerkar, R. S., Jalan, A. K., & Belgamwar, S. U. (2019). Fault diagnosis of rolling element bearing based on artificial neural network. Journal of Mechanical Science and Technology, 33(2), 505–511. https://doi.org/https://doi.org/10.1007/s12206-019-0103-x

 Web of Science ®Google Scholar

Sharma, A., Jigyasu, R., Mathew, L., & Chatterji, S. (2019). Bearing fault diagnosis using frequency domain features and artificial neural networks. Information and Communication Technology for Intelligent Systems, 107, 539–547. https://doi.org/https://doi.org/10.1007/978-981-13-1747-7

 Google Scholar

Bohorquez, J., Alexander, B., Simpson, A. R., & Lambert, M. F. (2020). Leak detection and topology identification in pipelines using fluid transients and artificial neural networks. Journal of Water Resources Planning and Management, 146(6), 04020040. https://doi.org/https://doi.org/10.1061/(ASCE)WR.1943-5452.0001187.

 Web of Science ®Google Scholar

Shaik, N. B., Pedapati, S. R., Taqvi, S. A. A., Othman, A. R., & Dzubir, F. A. A. (2020). A feed-forward back propagation neural network approach to predict the life condition of crude oil pipeline. Processes, 8(6), 661. https://doi.org/https://doi.org/10.3390/pr8060661

 Web of Science ®Google Scholar

Tavakoli, R., Sharifara, A., & Najafi, M. (2020). Artificial neural networks and adaptive neuro-fuzzy models to predict remaining useful life of water pipelines. World Environmental and Water Resources Congress, 191–204. https://doi.org/https://doi.org/10.1061/9780784482988.019

 Google Scholar

Laredo, D., Chen, Z., Schütze, O., & Sun, J. (2019). A neural network-evolutionary computational framework for remaining useful life estimation of mechanical systems. Neural Networks, 116, 178–187. https://doi.org/https://doi.org/10.1016/j.neunet.2019.04.016

 PubMed Web of Science ®Google Scholar

Shao, H., Jiang, H., Li, X., & Wu, S. (2018). Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine. Knowledge-Based Systems, 140, 1–14. https://doi.org/https://doi.org/10.1016/j.knosys.2017.10.024

 Web of Science ®Google Scholar

Wang, Z., Zheng, L., Wang, J., & Du, W. (2019). Research on novel bearing fault diagnosis method based on improved krill herd algorithm and kernel extreme learning machine. Complexity, 2019. Article ID 4031795. https://doi.org/https://doi.org/10.1155/2019/4031795

 Google Scholar

Chen, Z., Gryllias, K., & Li, W. (2019). Mechanical fault diagnosis using convolutional neural networks and extreme learning machine. Mechanical Systems and Signal Processing, 133, Article 106272. https://doi.org/https://doi.org/10.1016/j.ymssp.2019.106272

 Google Scholar

Liu, X., Huang, H., & Xiang, J. (2020). A personalized diagnosis method to detect faults in gears using numerical simulation and extreme learning machine. Knowledge-Based Systems, 195, Article 105653. https://doi.org/https://doi.org/10.1016/j.knosys.2020.105653

 Google Scholar

Wei, Y., Xu, M., Li, Y., & Huang, W. (2016). Gearbox fault diagnosis based on local mean decomposition, permutation entropy and extreme learning machine. Journal of Vibroengineering, 18(3), 1459–1473. https://doi.org/https://doi.org/10.21595/jve.2016.16567

 Web of Science ®Google Scholar

Jiang, W., Li, Z., Li, J., Zhu, Y., & Zhang, P. (2019). Study on a fault identification method of the hydraulic pump based on a combination of voiceprint characteristics and extreme learning machine. Processes, 7(2), 894. https://doi.org/https://doi.org/10.3390/pr7120894

 Web of Science ®Google Scholar

Rinanto, N., Adhitya, R. Y., Sarena, S. T., Kautsar, S., Munadhif, L., Setyoko, A. S., Syai'in, M., & Soeprijanto, A. (2016). Rotor bars fault detection by DFT spectral analysis and extreme learning machine. In 2016 International symposium on electronics and smart devices (pp. 103–108). IEEE.

 Google Scholar

Zhao, X., Jia, M., Ding, P., Yang, C., She, D., & Liu, Z. (2020). Intelligent fault diagnosis of multi-channel motor-rotor system based on multi-manifold deep extreme learning machine. IEEE/ASME Transactions on Mechatronics, 25(5), 2177–2187. https://doi.org/https://doi.org/10.1109/TMECH.2020.3004589

 Web of Science ®Google Scholar

Gong, W., Chen, H., Zhang, Z., Zhang, M., Wang, R., Guan, C., & Wang, Q. (2019). A novel deep learning method for intelligent fault diagnosis of rotating machinery based on improved CNN-SVM and multichannel data fusion. Sensors, 19(7), 1693. https://doi.org/https://doi.org/10.3390/s19071693.

 PubMed Web of Science ®Google Scholar

Liu, Z., Guo, W., Hu, J., & Ma, W. (2017). A hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT, KPCA and twin SVM. ISA Transactions, 66, 249–261. https://doi.org/https://doi.org/10.1016/j.isatra.2016.11.001

 PubMed Web of Science ®Google Scholar

Yao, D., Yang, J., Cheng, X., & Wang, X. (2018). Railway rolling bearing fault diagnosis based on muti-scale IMF permutation entropy and SA-SVM classifier. Journal of Mechanical Engineering, 54, 168–176. https://doi.org/https://doi.org/10.3901/JME.2018.09.168

 Google Scholar

Zhang, X. L., Wang, B. J., & Chen, X. F. (2015). Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine. Knowledge-Based Systems, 89, 56–85. https://doi.org/https://doi.org/10.1016/j.knosys.2015.06.017

 Web of Science ®Google Scholar

Zheng, J. D., Pan, H. Y., & Cheng, J. S. (2017). Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines. Mechanical Systems and Signal Processing, 85, 746–759. https://doi.org/https://doi.org/10.1016/j.ymssp.2016.09.010

 Web of Science ®Google Scholar

Han, D., Zhao, N., & Shi, P. (2019). Gear fault feature extraction and diagnosis method under different load excitation based on EMD, PSO-SVM and fractal box dimension. Journal of Mechanical Science and Technology, 33(2), 487–494. https://doi.org/https://doi.org/10.1007/s12206-019-0101-z

 Web of Science ®Google Scholar

Yang, D., Liu, Y., Li, S., Li, X., & Ma, L. (2015). Gear fault diagnosis based on support vector machine optimized by artificial bee colony algorithm. Mechanism and Machine Theory, 90, 219–229. https://doi.org/https://doi.org/10.1016/j.mechmachtheory.2015.03.013

 Web of Science ®Google Scholar

Gangsar, P., & Tiwari, R. (2017). Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms. Mechanical Systems and Signal Processing, 94, 464–481. https://doi.org/https://doi.org/10.1016/j.ymssp.2017.03.016

 Web of Science ®Google Scholar

Keskes, H., & Braham, A. (2015). Recursive undecimated wavelet packet transform and DAG SVM for induction motor diagnosis. IEEE Transactions on Industrial Informatics, 11(5), 1059–1066. https://doi.org/https://doi.org/10.1109/TII.2015.2462315

 Web of Science ®Google Scholar

Wang, C., Han, F., Zhang, Y., & Lu, J. (2020). An SAE-based resampling SVM ensemble learning paradigm for pipeline leakage detection. Neurocomputing, 403, 237–246. https://doi.org/https://doi.org/10.1016/j.neucom.2020.04.105

 Web of Science ®Google Scholar

Xiao, R., Hu, Q., & Li, J. (2019). Leak detection of gas pipelines using acoustic signals based on wavelet transform and support vector machine. Measurement, 146, 479–489. https://doi.org/https://doi.org/10.1016/j.measurement.2019.06.050

 Web of Science ®Google Scholar

Chen, Z., & Li, W. (2017). Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network. IEEE Transactions on Instrumentation and Measurement, 66(7), 1693–1702. https://doi.org/https://doi.org/10.1109/TIM.2017.2669947

 Web of Science ®Google Scholar

Jia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016). Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing, 72-73, 303–315. https://doi.org/https://doi.org/10.1016/j.ymssp.2015.10.025

 Web of Science ®Google Scholar

Lu, C., Wang, Z., Qin, W., & Ma, J. (2017). Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Processing, 130, 377–388. https://doi.org/https://doi.org/10.1016/j.sigpro.2016.07.028

 Web of Science ®Google Scholar

Meng, Z., Zhan, X., Li, J., & Pan, Z. (2018). An enhancement denoising autoencoder for rolling bearing fault diagnosis. Measurement, 130, 448–454. https://doi.org/https://doi.org/10.1016/j.measurement.2018.08.010

 Web of Science ®Google Scholar

Ren, L., Sun, Y., Cui, J., & Zhang, L. (2018). Bearing remaining useful life prediction based on deep autoencoder and deep neural networks. Journal of Manufacturing Systems, 48, 71–77. https://doi.org/https://doi.org/10.1016/j.jmsy.2018.04.008

 Web of Science ®Google Scholar

Tan, J., Lu, W., An, J., & Wan, X. (2015). Fault diagnosis method study in roller bearing based on wavelet transform and stacked auto-encoder. In The 27th Chinese control and decision conference (2015 CCDC) (pp. 4608–4613). IEEE.

 Google Scholar

Wang, J., Li, S., An, Z., Jiang, X., Qian, W., & Ji, S. (2019). Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines. Neurocomputing, 329, 53–65. https://doi.org/https://doi.org/10.1016/j.neucom.2018.10.049

 Web of Science ®Google Scholar

Wen, L., Gao, L., & Li, X. (2019). A new deep transfer learning based on sparse auto-encoder for fault diagnosis. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(1), 136–144. https://doi.org/https://doi.org/10.1109/TSMC.2017.2754287

 Web of Science ®Google Scholar

Zhao, X., & Jia, M. (2019). A new local-global deep neural network and its application in rotating machinery fault diagnosis. Neurocomputing, 366, 215–233. https://doi.org/https://doi.org/10.1016/j.neucom.2019.08.010

 Web of Science ®Google Scholar

Li, F., Pang, X., & Yang, Z. (2019). Motor current signal analysis using deep neural networks for planetary gear fault diagnosis. Measurement, 145, 45–54. https://doi.org/https://doi.org/10.1016/j.measurement.2019.05.074

 Web of Science ®Google Scholar

Li, Y., Cheng, G., Liu, C., & Chen, X. (2018). Study on planetary gear fault diagnosis based on variational mode decomposition and deep neural networks. Measurement, 130, 94–104. https://doi.org/https://doi.org/10.1016/j.measurement.2018.08.002

 Web of Science ®Google Scholar

Liu, G., Bao, H., & Han, B. (2018). A stacked autoencoder-based deep neural network for achieving gearbox fault diagnosis. Mathematical Problems in Engineering, 2018. Article ID 5105709. https://doi.org/https://doi.org/10.1155/2018/5105709

 Google Scholar

Wang, J., Jiang, X., Li, S., & Xin, Y. (2017). A novel feature representation method based on deep neural networks for gear fault diagnosis. In 2017 Prognostics and system health management conference (PHM-Harbin) (pp. 1–6). IEEE.

 Google Scholar

Sun, W., Shao, S., Zhao, R., Yan, R., Zhang, X., & Chen, X. (2016). A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement, 89, 171–178. https://doi.org/https://doi.org/10.1016/j.measurement.2016.04.007

 Web of Science ®Google Scholar

Zadkarami, M., Safavi, A. A., Taheri, M., & Salimi, F. F. (2020). Data driven leakage diagnosis for oil pipelines: An integrated approach of factor analysis and deep neural network classifier. Transactions of the Institute of Measurement and Control, 42(14), 2708–2718. https://doi.org/https://doi.org/10.1177/0142331220928145

 Web of Science ®Google Scholar

Che, C., Wang, H., Ni, X., & Fu, Q. (2020). Domain adaptive deep belief network for rolling bearing fault diagnosis. Computers and Industrial Engineering, 143, Article 106427. https://doi.org/https://doi.org/10.1016/j.cie.2020.106427

 Google Scholar

Li, J., Li, X., He, D., & Qu, Y. (2020). Unsupervised rotating machinery fault diagnosis method based on integrated SAE-DBN and a binary processor. Journal of Intelligent Manufacturing, 31(8), 1899–1916. https://doi.org/https://doi.org/10.1007/s10845-020-01543-8

 Web of Science ®Google Scholar

Yan, X., Liu, Y., & Jia, M. (2020). Multiscale cascading deep belief network for fault identification of rotating machinery under various working conditions. Knowledge-Based Systems, 193, Article 105484. https://doi.org/https://doi.org/10.1016/j.knosys.2020.105484

 Google Scholar

Yu, K., Lin, T. R., & Tan, J. (2020). A bearing fault and severity diagnostic technique using adaptive deep belief networks and dempster-shafer theory. Structural Health Monitoring, 19(1), 240–261. https://doi.org/https://doi.org/10.1177/1475921719841690

 Web of Science ®Google Scholar

Zhu, J., Hu, T., Jiang, B., & Yang, X. (2020). Intelligent bearing fault diagnosis using PCA-DBN framework. Neural Computing and Applications, 32, 10773–10781. https://doi.org/https://doi.org/10.1007/s00521-019-04612-z

 Web of Science ®Google Scholar

Chen, H., Wang, J., Tang, B., Xiao, K., & Li, J. (2016). An integrated approach to planetary gearbox fault diagnosis using deep belief networks. Measurement Science and Technology, 28(2), 025010.

 Web of Science ®Google Scholar

Gai, J., Shen, J., Wang, H., & Hu, Y. (2020). A parameter-optimized DBN using GOA and its application in fault diagnosis of gearbox. Shock and Vibration, 2020. Article ID: 4294095. https://doi.org/https://doi.org/10.1155/2020/4294095

 Google Scholar

He, J., Yang, S., & Gan, C. (2017). Unsupervised fault diagnosis of a gear transmission chain using a deep belief network. Sensors, 17(7), 1564. https://doi.org/https://doi.org/10.3390/s17071564

 Web of Science ®Google Scholar

Qin, Y., Wang, X., & Zou, J. (2019). The optimized deep belief networks with improved logistic sigmoid units and their application in fault diagnosis for planetary gearboxes of wind turbines. IEEE Transactions on Industrial Electronics, 66(5), 3814–3824. https://doi.org/https://doi.org/10.1109/TIE.2018.2856205

 Web of Science ®Google Scholar

Wang, S., Xiang, J., Zhong, Y., & Tang, H. (2018). A data indicator-based deep belief networks to detect multiple faults in axial piston pumps. Mechanical Systems and Signal Processing, 112, 154–170. https://doi.org/https://doi.org/10.1016/j.ymssp.2018.04.038

 Web of Science ®Google Scholar

Guo, Y., Fan, Q., & Liu, X. (2019). Research on leakage diagnosis of heating pipe network based on deep belief network. In 2019 IEEE international conference on industrial internet (ICII) (pp. 303–304). IEEE.

 Google Scholar

Huang, J., Wang, X., Wang, D., Wang, Z., & Hua, X. (2019). Analysis of weak fault in hydraulic system based on multi-scale permutation entropy of fault-sensitive intrinsic mode function and deep belief network. Entropy, 21(4), 425. https://doi.org/https://doi.org/10.3390/e21040425

 Web of Science ®Google Scholar

Lang, X., Hu, Z., Li, P., Li, Y., Cao, J., & Ren, H. (2018). Pipeline leak aperture recognition based on wavelet packet analysis and a deep belief network with ICR. Wireless Communications and Mobile Computing, 2018, Article 6934825. https://doi.org/https://doi.org/10.1155/2018/6934825

 Google Scholar

Zhang, T., Li, Z., Deng, Z., & Hu, B. (2019). Hybrid data fusion DBN for intelligent fault diagnosis of vehicle reducers. Sensors, 19(11), 2504. https://doi.org/https://doi.org/10.3390/s19112504

 Web of Science ®Google Scholar

Guo, S., Zhang, B., Yang, T., Lyu, D., & Gao, W. (2020). Multitask convolutional neural network with information fusion for bearing fault diagnosis and localization. IEEE Transactions on Industrial Electronics, 67(9), 8005–8015. https://doi.org/https://doi.org/10.1109/TIE.41

 Web of Science ®Google Scholar

Wen, L., Li, X., Gao, L., & Zhang, Y. (2018). A new convolutional neural network-based data-driven fault diagnosis method. IEEE Transactions on Industrial Electronics, 65(7), 5990–5998. https://doi.org/https://doi.org/10.1109/TIE.2017.2774777

 Web of Science ®Google Scholar

Yang, B., Liu, R., & Zio, E. (2019). Remaining useful life prediction based on a double-convolutional neural network architecture. IEEE Transactions on Industrial Electronics, 66(12), 9521–9530. https://doi.org/https://doi.org/10.1109/TIE.41

 Web of Science ®Google Scholar

Zhu, J., Chen, N., & Peng, W. (2019). Estimation of bearing remaining useful life based on multiscale convolutional neural network. IEEE Transactions on Industrial Electronics, 66(4), 3208–3216. https://doi.org/https://doi.org/10.1109/TIE.2018.2844856

 Web of Science ®Google Scholar

Grezmaka, J., Wang, P., Sun, C., & Gao, R. X. (2019). Explainable convolutional neural network for gearbox fault diagnosis. Procedia CIRP, 80, 476–481. https://doi.org/https://doi.org/10.1016/j.procir.2018.12.008

 Google Scholar

Li, T., Zhao, Z., Sun, C., Yan, R., & Chen, X. (2020). Multi-scale CNN for multi-sensor feature fusion in helical gear fault detection. Procedia Manufacturing, 49, 89–93. https://doi.org/https://doi.org/10.1016/j.promfg.2020.07.001

 Google Scholar

Wu, C., Jiang, P., Ding, C., Feng, F., & Chen, T. (2019). Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network. Computers in Industry, 108, 53–61. https://doi.org/https://doi.org/10.1016/j.compind.2018.12.001

 Web of Science ®Google Scholar

Ince, T., Kiranyaz, S., Eren, L., Askar, M., & Gabbouj, M. (2016). Real-time motor fault detection by 1-D convolutional neural networks. IEEE Transactions on Industrial Electronics, 63(11), 7067–7075. https://doi.org/https://doi.org/10.1109/TIE.2016.2582729

 Web of Science ®Google Scholar

Shao, S., Yan, R., Lu, Y., Wang, P., & Gao, R. X. (2020). DCNN-based multi-signal induction motor fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 69(6), 2658–2669. https://doi.org/https://doi.org/10.1109/TIM.19

 Web of Science ®Google Scholar

Wang, F., Liu, R., Hu, Q., & Chen, X. (2021). Cascade convolutional neural network with progressive optimization for motor fault diagnosis under non-stationary conditions. IEEE Transactions on Industrial Informatics, 17(4), 2511–2521. https://doi.org/https://doi.org/10.1109/TII.9424

 Web of Science ®Google Scholar

Xie, Y., Xiao, Y., Liu, X., Liu, G., Jiang, W., & Qin, J. (2020). Time-frequency distribution map-based convolutional neural network (CNN) model for underwater pipeline leakage detection using acoustic signals. Sensors, 20(18), 5040. https://doi.org/https://doi.org/10.3390/s20185040

 Web of Science ®Google Scholar

An, Z., Li, S., Wang, J., & Jiang, X. (2020). A novel bearing intelligent fault diagnosis framework under time-varying working conditions using recurrent neural network. ISA Transactions, 100, 155–170. https://doi.org/https://doi.org/10.1016/j.isatra.2019.11.010

 PubMed Web of Science ®Google Scholar

Guo, L., Li, N., Jia, F., Lei, Y., & Lin, J. (2017). A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing, 240, 98–109. https://doi.org/https://doi.org/10.1016/j.neucom.2017.02.045

 Web of Science ®Google Scholar

Li, F., Chen, Y., Wang, J., Zhou, X., & Tang, B. (2019). A reinforcement learning unit matching recurrent neural network for the state trend prediction of rolling bearings. Measurement, 145, 191–203. https://doi.org/https://doi.org/10.1016/j.measurement.2019.05.093

 Web of Science ®Google Scholar

Liu, H., Zhou, J., Zheng, Y., Jiang, W., & Zhang, Y. (2018). Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. ISA Transactions, 77, 167–178. https://doi.org/https://doi.org/10.1016/j.isatra.2018.04.005

 PubMed Web of Science ®Google Scholar

Peng, Y., Cheng, F., Qiao, W., & Qu, L. (2017). Fault prognosis of drivetrain gearbox based on a recurrent neural network. In 2017 IEEE international conference on electro information technology (EIT) (pp. 593–599). IEEE.

 Google Scholar

Yin, A., Yan, Y., Zhang, Z., Li, C., & Sánchez, R. V. (2020). Fault diagnosis of wind turbine gearbox based on the optimized LSTM neural network with cosine loss. Sensors, 20(8), 2339. https://doi.org/https://doi.org/10.3390/s20082339

 Web of Science ®Google Scholar

Yu, J., Xu, Y., & Liu, K. (2019). Planetary gear fault diagnosis using stacked denoising autoencoder and gated recurrent unit neural network under noisy environment and time-varying rotational speed conditions. Measurement Science and Technology, 30(9), 095003.

 Web of Science ®Google Scholar

Xiao, D., Huang, Y., Qin, C., Shi, H., & Li, Y. (2019). Fault diagnosis of induction motors using recurrence quantification analysis and LSTM with weighted BN. Shock and Vibration, 2019. Article ID 8325218. https://doi.org/https://doi.org/10.1155/2019/8325218

 Google Scholar

Ye, S., Jiang, J., Li, J., Liu, Y., Zhou, Z., & Liu, C. (2020). Fault diagnosis and tolerance control of five-level nested NPP converter using wavelet packet and LSTM. IEEE Transactions on Power Electronics, 35(2), 1907–1921. https://doi.org/https://doi.org/10.1109/TPEL.63

 Web of Science ®Google Scholar