Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing
Tài liệu tham khảo
Shao, 2018, Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing, Mech. Syst. Signal Process., 100, 743, 10.1016/j.ymssp.2017.08.002
Li, 2017, A Bayesian approach to consequent parameter estimation in probabilistic fuzzy systems and its application to bearing fault classification, Knowl.-Based Syst., 129, 39, 10.1016/j.knosys.2017.05.007
Lei, 2018, Machinery health prognostics: A systematic review from data acquisition to RUL prediction, Mech. Syst. Signal Process., 104, 799, 10.1016/j.ymssp.2017.11.016
Kan, 2015, A review on prognostic techniques for non-stationary and non-linear rotating systems, Mech. Syst. Signal Process., 62–63, 1, 10.1016/j.ymssp.2015.02.016
Guo, 2017, A recurrent neural network based health indicator for remaining useful life prediction of bearings, Neurocomputing, 240, 98, 10.1016/j.neucom.2017.02.045
Heng, 2009, Rotating machinery prognostics: state of the art, challenges and opportunities, Mech. Syst. Signal Process., 23, 724, 10.1016/j.ymssp.2008.06.009
Yu, 2011, Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models, Mech. Syst. Signal Process., 25, 2573, 10.1016/j.ymssp.2011.02.006
He, 2013, Vibration signal classification by wavelet packet energy flow manifold learning, J. Sound Vib., 332, 1881, 10.1016/j.jsv.2012.11.006
Wei, 2017, A novel intelligent method for bearing fault diagnosis based on affinity propagation clustering and adaptive feature selection, Knowl.-Based Syst., 116, 1, 10.1016/j.knosys.2016.10.022
Yang, 2006, A roller bearing fault diagnosis method based on EMD energy entropy and ANN, J. Sound Vib., 294, 269, 10.1016/j.jsv.2005.11.002
Liu, 2018, A novel fault diagnosis method based on noise-assisted MEMD and functional neural fuzzy network for rolling element bearings, IEEE Access, 6, 27048, 10.1109/ACCESS.2018.2833851
Bin, 2012, Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network, Mech. Syst. Signal Process., 27, 696, 10.1016/j.ymssp.2011.08.002
Piotrkowski, 2009, Wavelet power, entropy and bispectrum applied to AE signals for damage identification and evaluation of corroded galvanized steel, Mech. Syst. Signal Process., 23, 432, 10.1016/j.ymssp.2008.05.006
Yan, 2019, Intelligent fault diagnosis of rotating machinery using improved multiscale dispersion entropy and mRMR feature selection, Knowl.-Based Syst., 163, 450, 10.1016/j.knosys.2018.09.004
Li, 2018, Early fault diagnosis of rolling bearings based on hierarchical symbol dynamic entropy and binary tree support vector machine, J. Sound Vib., 428, 72, 10.1016/j.jsv.2018.04.036
Shao, 2018, Rolling bearing fault detection using continuous deep belief network with locally linear embedding, Comput. Ind., 96, 27, 10.1016/j.compind.2018.01.005
Zhao, 2019, Deep learning and its applications to machine health monitoring, Mech. Syst. Signal Process., 115, 213, 10.1016/j.ymssp.2018.05.050
LeCun, 2015, Review: Deep learning, Nature, 521, 436, 10.1038/nature14539
Längkvist, 2014, A review of unsupervised feature learning and deep learning for time-series modeling, Pattern Recognit. Lett., 42, 11, 10.1016/j.patrec.2014.01.008
Shao, 2017, Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet, ISA Trans., 69, 187, 10.1016/j.isatra.2017.03.017
Tamilselvan, 2013, Failure diagnosis using deep belief learning based health state classification, Reliab. Eng. Syst. Saf., 115, 124, 10.1016/j.ress.2013.02.022
Shao, 2018, Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine, Knowl.-Based Syst., 140, 1, 10.1016/j.knosys.2017.10.024
Shen, 2018, An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder, Eng. Appl. Artif. Intell., 76, 170, 10.1016/j.engappai.2018.09.010
Jiao, 2018, A multivariate encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes, Knowl.-Based Syst., 160, 237, 10.1016/j.knosys.2018.07.017
Han, 2019, A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults, Knowl.-Based Syst., 165, 474, 10.1016/j.knosys.2018.12.019
Jiang, 2018, Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network, Meas. Sci. Technol., 29, 10.1088/1361-6501/aab945
Zhao, 2017, Learning to monitor machine health with convolutional bi-directional LSTM networks, Sensors, 17, 273, 10.3390/s17020273
Greff, 2017, LSTM: A search space odyssey, IEEE Trans. Neural Netw. Learn. Syst., 28, 2222, 10.1109/TNNLS.2016.2582924
Zhao, 2018, Machine health monitoring using local feature-based gated recurrent unit networks, IEEE Trans. Ind. Electron., 65, 1539, 10.1109/TIE.2017.2733438
Li, 2019, Rolling bearing health prognosis using a modified health index based hierarchical gated recurrent unit network, Mech. Mach. Theory, 133, 229, 10.1016/j.mechmachtheory.2018.11.005
Ma, 2017, Locally linear embedding on grassmann manifold for performance degradation assessment of bearings, IEEE Trans. Reliab., 66, 467, 10.1109/TR.2017.2691730
Wang, 2010, Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform, Mech. Syst. Signal Process., 24, 119, 10.1016/j.ymssp.2009.06.015
Qu, 2016, A novel intelligent method for mechanical fault diagnosis based on dual-tree complex wavelet packet transform and multiple classifier fusion, Neurocomputing, 171, 837, 10.1016/j.neucom.2015.07.020
Loshchilov, 2017, SGDR: Stochastic gradient descent with warm restarts
Bello, 2017, Neural optimizer search with reinforcement learning
Bearing Data Set, http://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/.
Zheng, 2018, Generalized composite multiscale permutation entropy and Laplacian score based rolling bearing fault diagnosis, Mech. Syst. Signal Process., 99, 229, 10.1016/j.ymssp.2017.06.011
Nectoux, 2012, PRONOSTIA : An experimental platform for bearings accelerated degradation tests, 1
IEEE PHM 2012 Prognostic Challenge. Experiments, Scoring of Results, Winners. Available: http://www.femto-st.fr/f/d/IEEEPHM2012-Challenge-Details.pdf.
Li, 2015, An improved exponential model for predicting remaining useful life of rolling element bearings, IEEE Trans. Ind. Electron., 62, 7762, 10.1109/TIE.2015.2455055