References
- 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.
- 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
- 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
- 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.
- Alves, M. V. C., Barbosa, J. R., Prata, A. T., & Ribas, F. A. (2011). Fluid flow in a screw pump oil supply system for reciprocating compressors. International Journal of Refrigeration, 34(1), 74–83. https://doi.org/https://doi.org/10.1016/j.ijrefrig.2010.08.003
- 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
- Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174–188. https://doi.org/https://doi.org/10.1109/78.978374
- 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
- Aziz, S., Khan, M. U., Aamir, F., & Javid, M. A. (2019). Electromyography (EMG) data-driven load classification using empirical mode decomposition and feature analysis. In 2019 International conference on frontiers of information technology (FIT) (pp. 272–2725). IEEE.
- 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
- 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.
- Blunt, S. D., Metcalf, J., Jakabosky, J., Stiles, J., & Himed, B. (2017). Multi-waveform space-time adaptive processing. IEEE Transactions on Aerospace and Electronic Systems, 53(1), 385–404. https://doi.org/https://doi.org/10.1109/TAES.2017.2650639
- 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.
- Carpenter, J., Clifford, P., & Fearnhead, P. (1999). Improved particle filter for nonlinear problems. IEE Proceedings – Radar, Sonar and Navigation, 146(1), 2–7. https://doi.org/https://doi.org/10.1049/ip-rsn:19990255
- 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
- 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
- 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
- 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.
- Chen, J., Li, Z., Pan, J., Chen, G., Zi, Y., Yuan, J., Chen, B., & He, Z. (2016). Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 70–71, 1–35. https://doi.org/https://doi.org/10.1016/j.ymssp.2015.08.023
- 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
- 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.
- 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
- 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
- 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
- Choi, G., Oh, H., & Kim, D. (2018). Enhancement of variational mode decomposition with missing values. Signal Process, 142, 75–86. https://doi.org/https://doi.org/10.1016/j.sigpro.2017.07.007
- Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
- 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
- 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
- Dai, C., Luo, G., & Long, Z. (2017). A signal process algorithm of relative position detection sensor for high speed maglev trains based on KF-UKF. In IEEE international conference on signal processing, communications and computing (ICSPCC) (pp. 1–6). IEEE.
- Das, K., Nath, D., & Pradhan, S. N. (2020). FPGA and ASIC realisation of EMD algorithm for real-time signal processing. IET Circuits, Devices and Systems, 14(6), 741–749. https://doi.org/https://doi.org/10.1049/cds2.v14.6
- Daubechies, I. (1990). The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory, 36(5), 961–1005. https://doi.org/https://doi.org/10.1109/18.57199
- 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
- Ding, D., Han, Q. L., Wang, Z., & Ge, X. (2019). A survey on model-based distributed control and filtering for industrial cyber-physical systems. IEEE Transactions on Industrial Informatics, 15(5), 2483–2499. https://doi.org/https://doi.org/10.1109/TII.9424
- Ding, D., Wang, Z., & Han, Q. (2020). A set-membership approach to event-triggered filtering for general nonlinear systems over sensor networks. IEEE Transactions on Automatic Control, 65(4), 1792–1799. https://doi.org/https://doi.org/10.1109/TAC.9
- Dragomiretskiy, K., & Zosso, D. (2014). Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3), 531–544. https://doi.org/https://doi.org/10.1109/TSP.2013.2288675
- Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. https://doi.org/https://doi.org/10.1207/s15516709cog1402_1
- Elsayed, W. T., Hegazy, Y. G., El-bages, M. S., & Bendary, F. M. (2017). Improved random drift particle swarm optimization with self-adaptive mechanism for solving the power economic dispatch problem. IEEE Transactions on Industrial Informatics, 13(3), 1017–1026. https://doi.org/https://doi.org/10.1109/TII.2017.2695122
- Feng, J., Lei, Y., Guo, L., Lin, J., & Xing, S. (2018). A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines. Neurocomputing, 272, 619–628. https://doi.org/https://doi.org/10.1016/j.neucom.2017.07.032
- 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
- Fujiyoshi, H., Hirakawa, T., & Yamashita, T. (2019). Deep learning-based image recognition for autonomous driving. IATSS Research, 43(4), 244–252. https://doi.org/https://doi.org/10.1016/j.iatssr.2019.11.008
- Gabor, D. (1946). Theory of communication. Part 1: The analysis of information. Journal of the Institution of Electrical Engineers – Part III: Radio and Communication Engineering, 93(26), 429–441.
- 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
- 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
- Ge, X., Han, Q.-L., & Wang, Z. (2019). A threshold-parameter-dependent approach to designing distributed event-triggered H∞ consensus filters over sensor networks. IEEE Transactions on Cybernetics, 49(4), 1148–1159. https://doi.org/https://doi.org/10.1109/TCYB.6221036
- Georg, H., & Matthias, R. (2018). Deep learning for fault detection in wind turbines. Renewable and Sustainable Energy Reviews, 98, 189–198. https://doi.org/https://doi.org/10.1016/j.rser.2018.09.012
- 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.
- Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Proceedings of the advances in neural information proceeding systems (pp. 2672–2680). MIT Press.
- Graves, A., Mohamed, A., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 6645–6649). IEEE.
- 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
- Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., & Chen, T. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354–377. https://doi.org/https://doi.org/10.1016/j.patcog.2017.10.013
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- Heng, A., Zhang, S., Tan, A. C. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23(3), 724–739. https://doi.org/https://doi.org/10.1016/j.ymssp.2008.06.009
- Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554. https://doi.org/https://doi.org/10.1162/neco.2006.18.7.1527
- Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/https://doi.org/10.1162/neco.1997.9.8.1735
- Hong, P., Hu, C., Si, X., Zhang, J., Pang, Z., & Zhang, P. (2019). Review of machine learning based remaining useful life prediction methods for equipment. Journal of Mechanical Engineering, 55(8), 1–13.
- Hu, J., Wang, Z., Alsaadi, F. E., & Hayat, T. (2017). Event-based filtering for time-varying nonlinear systems subject to multiple missing measurements with uncertain missing probabilities. Information Fusion, 38, 74–83. https://doi.org/https://doi.org/10.1016/j.inffus.2017.03.003
- Huang, G., Huang, G. B., Song, S., & You, K. (2015). Trends in extreme learning machines: A review. Neural Networks, 61, 32–48. https://doi.org/https://doi.org/10.1016/j.neunet.2014.10.001
- Huang, G., Zhu, Q., & Siew, C. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1-3), 489–501. https://doi.org/https://doi.org/10.1016/j.neucom.2005.12.126
- 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
- Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N., Tung, C. C., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454(1971), 903–995. https://doi.org/https://doi.org/10.1098/rspa.1998.0193
- 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
- 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
- Jain, A. K., Mao, J., & Mohiuddin, K. M. (1996). Artificial neural networks: A tutorial. Computer, 29(3), 31–44. https://doi.org/https://doi.org/10.1109/2.485891
- 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
- 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
- Jondhale, S. R., & Deshpande, R. S. (2019a). GRNN and KF framework based real time target tracking using PSOC BLE and smartphone. Ad Hoc Networks, 84, 19–28. https://doi.org/https://doi.org/10.1016/j.adhoc.2018.09.017
- Jondhale, S. R., & Deshpande, R. S. (2019b). Kalman filtering framework-based real time target tracking in wireless sensor networks using generalized regression neural networks. IEEE Sensors Journal, 19(1), 224–233. https://doi.org/https://doi.org/10.1109/JSEN.2018.2873357
- Jordan, M. I. (1986). Serial order: A parallel distributed processing approach. Institute for Cognitive Science Report.
- 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
- Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35–45. https://doi.org/https://doi.org/10.1115/1.3662552
- Kalman, R. E., & Bucy, R. S. (1961). New results in linear filtering and prediction theory. Journal of Fluids Engineering, 83(1), 95–108. https://doi.org/https://doi.org/10.1115/1.3658902
- 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.
- 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
- 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
- Kira, K., & Rendell, L. A. (1992). The feature selection problem: Traditional methods and a new algorithm. In Tenth national conference on artificial intelligence. AAAI Press.
- 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
- 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
- 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
- Layouni, M., Hamdi, M. S., & Tahar, S. (2017). Detection and sizing of metal-loss defects in oil and gas pipelines using pattern-adapted wavelets and machine learning. Applied Soft Computing, 52, 247–261. https://doi.org/https://doi.org/10.1016/j.asoc.2016.10.040
- 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
- Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104, 799–834. https://doi.org/https://doi.org/10.1016/j.ymssp.2017.11.016
- 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
- Lei, Y., Lin, J., He, Z., & Zuo, M. J. (2013). A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 35(1-2), 108–126. https://doi.org/https://doi.org/10.1016/j.ymssp.2012.09.015
- Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138, Article 106587. https://doi.org/https://doi.org/10.1016/j.ymssp.2019.106587
- 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
- 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
- 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.
- Li, J., Dong, H., Wang, Z., & Bu, X. (2020). Partial-neurons-based passivity-guaranteed state estimation for neural networks with randomly occurring time delays. IEEE Transactions on Neural Networks and Learning Systems, 31(9), 3747–3753. https://doi.org/https://doi.org/10.1109/TNNLS.5962385
- Li, J., Dong, H., Wang, Z., & Fei, W. (2020). Delay-distribution-dependent state estimation for neural networks under stochastic communication protocol with uncertain transition probabilities. Neural Networks, 130, 143–151. https://doi.org/https://doi.org/10.1016/j.neunet.2020.06.023
- Li, J., Dong, H., Wang, Z., & Zhang, W. (2018). Protocol-based state estimation for delayed Markovian jumping neural networks. Neural Networks, 108, 355–364. https://doi.org/https://doi.org/10.1016/j.neunet.2018.08.017
- 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
- Li, Q., Shen, B., Wang, Z., & Sheng, W. (2020). Recursive distributed filtering over sensor networks on Gilbert-Elliott channels: A dynamic event-triggered approach. Automatica, 113. Article 108681. https://doi.org/https://doi.org/10.1016/j.automatica.2019.108681
- Li, Q., Zheng, B., Tu, B., Wang, J., & Zhou, C. (2020). Ensemble EMD-based spectral-spatial feature extraction for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5134–5148. https://doi.org/https://doi.org/10.1109/JSTARS.4609443
- 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
- Li, X., Han, F., Hou, N., Dong, H., & Liu, H. (2020). Set-membership filtering for piecewise linear systems with censored measurements under Round-Robin protocol. International Journal of Systems Science, 51(9), 1578–1588. https://doi.org/https://doi.org/10.1080/00207721.2020.1768453
- 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
- 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
- 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
- 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
- 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
- Lim, C. K. R., & Mba, D. (2015). Switching Kalman filter for failure prognostic. Mechanical Systems and Signal Processing, 52–53, 426–435. https://doi.org/https://doi.org/10.1016/j.ymssp.2014.08.006
- 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
- 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
- Liu, L., Ma, L., Zhang, J., & Bo, Y. (2021). Distributed non-fragile set-membership filtering for nonlinear systems under fading channels and bias injection attacks. International Journal of Systems Science, 52(6), 1192–1205. https://doi.org/https://doi.org/10.1080/00207721.2021.1872118
- Liu, L., Rui, G., & Zhang, Y. (2020). Duffing oscillator weak signal detection method based on EMD signal processing. In 2020 International conference on computer information and big data applications (CIBDA) (pp. 495–498). IEEE.
- Liu, R., Yang, B., Zio, E., & Chen, X. (2018). Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108, 33–47. https://doi.org/https://doi.org/10.1016/j.ymssp.2018.02.016
- Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11–26. https://doi.org/https://doi.org/10.1016/j.neucom.2016.12.038
- Liu, W., Wang, Z., Tian, L., Lauria, S., & Liu, X. (2021). Melt pool segmentation for additive manufacturing: A generative adversarial network approach. Computers and Electrical Engineering, 92, Article 107183. https://doi.org/https://doi.org/10.1016/j.compeleceng.2021.107183
- Liu, W., Wang, Z., Yuan, Y., Zeng, N., Hone, K., & Liu, X. (2021). A novel sigmoid-function-based adaptive weighted particle swarm optimizer. IEEE Transactions on Cybernetics, 51(2), 1085–1093. https://doi.org/https://doi.org/10.1109/TCYB.6221036
- Liu, W., Wang, Z., Zeng, N., Alsaadi, F. E., & Liu, X. (2021). A PSO-based deep learning approach to classifying patients from emergency departments. International Journal of Machine Learning and Cybernetics, 12(7), 1939–1948. https://doi.org/https://doi.org/10.1007/s13042-021-01285-w
- 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
- Liu, Y., Yang, G., Lia, M., & Yin, H. (2016). Variational mode decomposition denoising combined the detrended fluctuation analysis. Signal Process, 125, 349–364. https://doi.org/https://doi.org/10.1016/j.sigpro.2016.02.011
- 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
- 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
- Ma, L., Wang, Z., Liu, Y., & Alsaadi, F. E. (2019). Distributed filtering for nonlinear time-delay systems over sensor networks subject to multiplicative link noises and switching topology. International Journal of Robust and Nonlinear Control, 29(10), 2941–2959. https://doi.org/https://doi.org/10.1002/rnc.v29.10
- 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
- Mezgec, S., & Seljak, B. K. (2017). NutriNet: A deep learning food and drink image recognition system for dietary assessment. Nutrients, 9(7), 657. https://doi.org/https://doi.org/10.3390/nu9070657
- 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
- Mikolov, T., Kombrink, S., Burget, L., Černocký, J., & Khudanpur, S. (2011). Extensions of recurrent neural network language model. In 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 5528–5531). IEEE.
- Mondal, A., Banerjee, P., & Tang, H. (2018). A novel feature extraction technique for pulmonary sound analysis based on EMD. Computer Methods and Programs in Biomedicine, 159, 199–209. https://doi.org/https://doi.org/10.1016/j.cmpb.2018.03.016
- 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.
- 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
- Nawab, S., Quatieri, T., & Lim, J. (1983). Signal reconstruction from short-time Fourier transform magnitude. IEEE Transactions on Acoustics, Speech, and Signal Processing, 31(4), 986–998. https://doi.org/https://doi.org/10.1109/TASSP.1983.1164162
- Nguyen, G., Dlugolinsky, S., Bobák, M., Tran, V., García, Á. L., Heredia, I., Malík, P., & Hluchý, L. (2019). Machine learning and deep learning frameworks and libraries for large-scale data mining: A survey. Artificial Intelligence Review, 52(1), 77–124. https://doi.org/https://doi.org/10.1007/s10462-018-09679-z
- Noda, K., Yamaguchi, Y., Nakadai, K., Okuno, H. G., & Ogata, T. (2015). Audio-visual speech recognition using deep learning. Applied Intelligence, 42(4), 722–737. https://doi.org/https://doi.org/10.1007/s10489-014-0629-7.
- Padmanabhan, J., & Premkumar, M. J. J. (2015). Machine learning in automatic speech recognition: A survey. IETE Technical Review, 32(4), 240–251. https://doi.org/https://doi.org/10.1080/02564602.2015.1010611
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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.
- Sarikaya, R., Hinton, G. E., & Deoras, A. (2014). Application of deep belief networks for natural language understanding. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(4), 778–784. https://doi.org/https://doi.org/10.1109/TASLP.2014.2303296
- 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
- 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
- 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
- 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
- Shen, B., Wang, Z., Wang, D., & Li, Q. (2020). State-saturated recursive filter design for stochastic time-varying nonlinear complex networks under deception attacks. IEEE Transactions on Neural Networks and Learning Systems, 31(10), 3788–3800. https://doi.org/https://doi.org/10.1109/TNNLS.5962385
- Shen, B., Wang, Z., Wang, D., Luo, J., Pu, H., & Peng, Y. (2019). Finite-horizon filtering for a class of nonlinear time-delayed systems with an energy harvesting sensor. Automatica, 100, 144–152. https://doi.org/https://doi.org/10.1016/j.automatica.2018.11.010
- Shen, Y., Wang, Z., Shen, B., & Dong, H. (2021). Outlier-resistant recursive filtering for multi-sensor multi-rate networked systems under weighted try-once-discard protocol. IEEE Transactions on Cybernetics, 51(10), 4897–4908. https://doi.org/https://doi.org/10.1109/TCYB.2020.3021194
- 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
- Son, J. B., Zhou, S. Y., Sankavaram, C., Zhang, Y., & Du, X. Y. (2016). Remaining useful life prediction based on noisy condition monitoring signals using constrained Kalman filter. Reliability Engineering and System Safety, 152, 38–50. https://doi.org/https://doi.org/10.1016/j.ress.2016.02.006
- Sun, H., He, Z., Zi, Y., Yuan, J., Wang, X., Chen, J., & He, S. (2014). Multiwavelet transform and its applications in mechanical fault diagnosis – a review. Mechanical Systems and Signal Processing, 43(1–2), 1–24. https://doi.org/https://doi.org/10.1016/j.ymssp.2013.09.015
- 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
- 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.
- 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
- Tian, E., Wang, Z., Zou, L., & Yue, D. (2019). Probabilistic-constrained filtering for a class of nonlinear systems with improved static event-triggered communication. International Journal of Robust and Nonlinear Control, 29(5), 1484–1498. https://doi.org/https://doi.org/10.1002/rnc.v29.5
- Traore, B. B., Foguem, B. K., & Tangara, F. (2018). Deep convolution neural network for image recognition. Ecological Informatics, 48, 257–268. https://doi.org/https://doi.org/10.1016/j.ecoinf.2018.10.002
- Ververidis, D., & Kotropoulos, C. (2005). Emotional speech classification using Gaussian mixture models and the sequential floating forward selection algorithm. In 2005 IEEE international conference on multimedia and expo (pp. 1500–1503). IEEE.
- 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
- Wang, C., Han, F., Shen, Y., Li, X., & Dong, H. (2020). Full-information particle swarm optimizer based on event-triggering strategy and its applications. Acta Automatica Sinica, 66, 1–13.
- 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
- Wang, C., Zhang, Y., Song, J., Liu, Q., & Dong, H. (2019). A novel optimized SVM algorithm based on PSO with saturation and mixed time-delays for classification of oil pipeline leak detection. Systems Science and Control Engineering, 7(1), 75–88. https://doi.org/https://doi.org/10.1080/21642583.2019.1573386
- 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
- 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.
- 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
- 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
- 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
- Wang, L., Wang, Z., Shen, B., & Wei, G. (2020). Recursive filtering with measurement fading: A multiple description coding scheme. IEEE Transactions on Automatic Control. https://doi.org/https://doi.org/10.1109/TAC.2020.3034196
- Wang, L., Wang, Z., Wei, G., & Alsaadi, F. E. (2019). Observer-based consensus control for discrete-time multi-agent systems with coding-decoding communication protocol. IEEE Transactions on Cybernetics, 49(12), 4335–4345. https://doi.org/https://doi.org/10.1109/TCYB.6221036
- Wang, M., Wang, Z., Dong, H., & Han, Q.-L. (2021, April). A novel framework for backstepping-based control of discrete-time strict-feedback nonlinear systems with multiplicative noises. IEEE Transactions on Automatic Control, 66(4), 1484–1496. https://doi.org/https://doi.org/10.1109/TAC.9
- 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
- Wang, X., Liu, X., Wang, Z., Li, R., & Wu, Y. (2020). SVM+KF target tracking strategy using the signal strength in wireless sensor networks. Sensors, 20(14), 3832. https://doi.org/https://doi.org/10.3390/s20143832.
- Wang, Y., Zhao, Y., & Addepalli, S. (2020). Remaining useful life prediction using deep learning approaches: A review. Procedia Manufacturing, 49, 81–88. https://doi.org/https://doi.org/10.1016/j.promfg.2020.06.015
- 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
- Wang, Y. H., Yeh, C. H., Young, H. W. V., Hu, K., & Lo, M. T. (2014). On the computational complexity of the empirical mode decomposition algorithm. Physica A: Statistical Mechanics and its Applications, 400, 159–167. https://doi.org/https://doi.org/10.1016/j.physa.2014.01.020
- Wang, Z., Dong, M., Qin, Y., Du, Y., Zhao, F., & Gu, L. (2017). Suspension system state estimation using adaptive Kalman filtering based on road classification. Vehicle System Dynamics, 55(3), 371–398https://doi.org/https://doi.org/10.1080/00423114.2016.1267374
- 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
- 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
- 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
- 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
- Wong, C. M., Wang, B., Wang, Z., Lao, K. F., Rosa, A., & Wan, F. (2020). Spatial filtering in SSVEP-based BCIs: Unified framework and new improvements. IEEE Transactions on Biomedical Engineering, 67(11), 3057–3072. https://doi.org/https://doi.org/10.1109/TBME.10
- Wu, B., Li, K., Ge, F., Huang, Z., Yang, M., Siniscalchi, S., & Lee, C. (2017). An end-to-end deep learning approach to simultaneous speech dereverberation and acoustic modeling for robust speech recognition. IEEE Journal of Selected Topics in Signal Processing, 11(8), 1289–1300. https://doi.org/https://doi.org/10.1109/JSTSP.2017.2756439
- 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
- Wu, D., King, J., Chuang, C., Lin, C., & Jung, T. (2018). Spatial filtering for EEG-based regression problems in brain-computer interface (BCI). IEEE Transactions on Fuzzy Systems, 26(2), 771–781. https://doi.org/https://doi.org/10.1109/TFUZZ.91
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Yeh, J., Shieh, J., & N. E. Huang (2010). Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method. Advances in Adaptive Data Analysis, 02(02), 135–156. https://doi.org/https://doi.org/10.1142/S1793536910000422
- 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
- 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
- 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
- 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.
- 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
- 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
- Zamuda, A. (2017). Adaptive constraint handling and success history differential evolution for CEC 2017 constrained real-parameter optimization. In 2017 IEEE congress on evolutionary computation (CEC) (pp. 2443–2450). IEEE.
- Zanni, L., Le Boudec, J., Cherkaoui, R., & Paolone, M. (2017). A prediction-error covariance estimator for adaptive Kalman filtering in step-varying processes: Application to power-system state estimation. IEEE Transactions on Control Systems Technology, 25(5), 1683–1697. https://doi.org/https://doi.org/10.1109/TCST.2016.2628716
- Zeng, N., Li, H., Wang, Z., Liu, W., Liu, S., Alsaadi, F. E., & Liu, X. (2021). Deep-reinforcement-learning-based images segmentation for quantitative analysis of gold immunochromatographic strip. Neurocomputing, 425, 173–180. https://doi.org/https://doi.org/10.1016/j.neucom.2020.04.001
- Zhang, C., Wang, L., & Li, H. (2020). Optimization method based on process control of a new-type hydraulic-motor hybrid beam pumping unit. Measurement, 158, Article 107716. https://doi.org/https://doi.org/10.1016/j.measurement.2020.107716
- 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
- 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
- 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
- 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
- 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
- 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
- Zhao, S., Shmaliy, Y. S., Shi, P., & Ahn, C. K. (2017). Fusion Kalman/UFIR filter for state estimation with uncertain parameters and noise statistics. IEEE Transactions on Industrial Electronics, 64(4), 3075–3083. https://doi.org/https://doi.org/10.1109/TIE.2016.2636814
- 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
- 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
- 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
- Zhao, Z., Wang, Z., Zou, L., & Guo, J. (2020). Set-membership filtering for time-varying complex networks with uniform quantisations over randomly delayed redundant channels. International Journal of Systems Science, 51(16), 3364–3377. https://doi.org/https://doi.org/10.1080/00207721.2020.1814898
- Zheng, J., & Pan, H. (2020). Mean-optimized mode decomposition: An improved EMD approach for non-stationary signal processing. ISA Transactions, 106, 392–401. https://doi.org/https://doi.org/10.1016/j.isatra.2020.06.011
- 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
- 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
- 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
- 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
- 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
- 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.
- Zou, L., Wang, Z., Dong, H., & Han, Q.-L. (in press-a). Energy-to-peak state estimation with intermittent measurement outliers: The single-output case. IEEE Transactions on Cybernetics. https://doi.org/https://doi.org/10.1109/TCYB.2021.3057545
- Zou, L., Wang, Z., Hu, J., & Dong, H. (in press-b). Ultimately bounded filtering subject to impulsive measurement outliers. IEEE Transactions on Automatic Control. https://doi.org/https://doi.org/10.1109/TAC.2021.3081256