A deep learning-based two-stage prognostic approach for remaining useful life of rolling bearing (original) (raw)
References
Wang X, Yang Z, Yan X (2018) Novel particle swarm optimization-based variational mode decomposition method for the fault diagnosis of complex rotating machinery. IEEE/ASME Trans Mechatron 23(1):68–79 Google Scholar
Khorram A, Khalooei M, Rezghi M (2021) End-to-end CNN + LSTM deep learning approach for bearing fault diagnosis. Appl Intell 51:736–751 Google Scholar
Yao D, Li B, Liu H, Yang J, Jia L (2021) Remaining useful life prediction of roller bearings based on improved 1D-CNN and simple recurrent unit. Measurement 175:109166 Google Scholar
Cheng C et al (2020) A deep learning-based remaining useful life prediction approach for bearings. IEEE/ASME Trans Mechatron 25(3):1243–1254 Google Scholar
Cheng Y, Zhu H, Wu J, Or SW, Shao X (2021) Remaining useful life prognosis based on ensemble long short-term memory neural network. IEEE Trans Instrum Meas 70:3503912 Google Scholar
Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237 Google Scholar
Liang Z, Gao J, Jiang H, Gao X, Gao Z, Wang R (2018) A similarity-based method for remaining useful life prediction based on operational reliability. Appl Intell 48:2983–2995 Google Scholar
Wang Y, Peng Y, Zi Y, Jin X, Tsui K (2016) A two-stage data-driven based prognostic aroach for bearing degradation problem. IEEE Trans Ind Informat 12(3):924–932 Google Scholar
Qian Y, Yan R, Hu S (2014) Bearing degradation evaluation using recurrence quantification analysis and Kalman filter. IEEE Trans Instrum Meas 63(11):2599–2610 Google Scholar
Kundu P, Darpe AK, Kulkarni MS (2019) Weibull accelerated failure time regression model for remaining useful life prediction of bearing working under multiple operating conditions. Mech Syst Signal Process 134:106302 Google Scholar
Hu Y, Li H, Shi P, Chai Z, Wang K, Xie X, Chen Z (2018) A prediction method for the real-time remaining useful life of wind turbine bearings based on the Wiener process. Renew Energ 127:452–460 Google Scholar
Wang W, Carr M, Xu W, Kobbacy K (2011) A model for residual life prediction based on Brownian motion with an adaptive drift. Microelectron Reliab 51:285–293 Google Scholar
Cui L, Wang X, Xu Y, Jiang H, Zhou J (2019) A novel switching unscented Kalman filter method for remaining useful life prediction of rolling bearing. Measurement 135:678–684 Google Scholar
Cheng Y et al (2019) Reliability prediction of machinery with multiple degradation characteristics using double-Wiener process and Monte Carlo algorithm. Mech Syst Sig Process 134:106333 Google Scholar
Chen C, Zhang B, Vachtsevanos G (2012) Prediction of machine health condition using neuro-fuzzy and Bayesian algorithms. IEEE Trans Instrum Meas 61(2):297–306 Google Scholar
Wu J, Su Y, Cheng Y, Shao X, Deng C, Liu C (2018) Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system. Appl Soft Comput 68:12–23 Google Scholar
Dong S, Luo T (2013) Bearing degradation process prediction based on the PCA and optimized LS-SVM model. Measurement 46:3143–3152 Google Scholar
Zhu H, Cheng J, Zhang C, Wu J, Shao X (2020) Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings. Appl Soft Comput 88:106060 Google Scholar
Huang K, Wu Y, Wang C, Xie Y, Yang C, Gui W (2021) A projective and discriminative dictionary learning for high-dimensional process monitoring with industrial applications. IEEE Trans Ind Inform 17(1):558–568 Google Scholar
He Z, Shao H, Zhong X, Zhao X (2020) Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions. Knowl-Based Syst 207:106396 Google Scholar
Zhou F, Yang S, Fujita H, Chen D, Wen C (2020) Deep learning fault diagnosis method based on global optimization GAN for unbalanced data. Knowl-Based Syst 187:104837 Google Scholar
Cheng Y, Hu K, Wu J, Zhu H, Shao X, Auto-encoder quasi-recurrent neural networks for remaining useful life prediction of engineering systems, IEEE/ASME Trans Mechatron, online, https://doi.org/10.1109/TMECH.2021.3079729
Hu C, Pei H, Si X, Du D, Pang Z, Wang X (2020) A prognostic model based on DBN and diffusion process for degrading bearing. IEEE Trans Ind Electron 67(10):8767–8777 Google Scholar
Chen D, Qin Y, Wang Y, Zhou J (2020) Health indicator construction by quadratic function-based deep convolutional auto-encoder and its application into bearing RUL prediction, ISA Trans, online, https://doi.org/10.1016/j.isatra.2020.12.052
Cheng H, Kong X, Chen G, Wang Q, Wang R (2020) Transferable convolutional neural network based remaining useful life prediction of bearing under multiple failure behaviors. Measurement 168:108286 Google Scholar
Chen Y, Peng G, Zhu Z, Li S (2019) A novel deep learning method based on attention mechanism for bearing remaining useful life prediction. Appl Soft Comput 86:105919 Google Scholar
Wang B, Lei Y, Li N, Yan T (2019) Deep separable convolutional network for remaining useful life prediction of machinery. Syst Sig Process 134:106330 Google Scholar
Wang H, Chen J, Qu J, Ni G (2020) A new approach for safety life prediction of industrial rolling bearing based on state recognition and similarity analysis. Saf Sci 122:104530 Google Scholar
Bing W, Xiong H, Li H (2017) Rolling bearing performance degradation condition recognition based on mathematical morphological fractal dimension and fuzzy C-means. Measurement 109:1–8 Google Scholar
Cheng YW, Zhu HP, Wu J, Shao XY (2019) Machine health monitoring using adaptive kernel spectral clustering and deep long short-term memory recurrent neural networks. IEEE Trans Ind Inform 15(2):987–997 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 Google Scholar
Wu B, Li W, Qiu MQ (2017) Remaining useful life prediction of bearing with vibration signals based on a novel indicator. Shock Vib 2017:8927937
Wang Y, Peng Y, Zi Y, Jin X, Tsui K (2016) A two-stage data-driven-based prognostic approach for bearing degradation problem. IEEE Trans Ind Inform 12(3):924–932 Google Scholar
Gan WS (2020) Fast fourier transform. In: Signal processing and image processing for acoustical imaging. Springer, Singapore, pp 17–20 Google Scholar
Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544 MathSciNetMATH Google Scholar
Rodrigues A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492–1496 Google Scholar
Goodfellow I, Bengio Y, Courville A (2016) Deep learning - volume 1. MIT Press, Cambridge, pp 367–415 MATH Google Scholar
Schmidhuber J (2015) Deep learning in neural networks: An overview. Neural networks 61:85–117 Google Scholar
Chen L, Liu X, Peng L et al (2021) Deep learning based multimodal complex human activity recognition using wearable devices. Appl Intell 51:4029–4042 Google Scholar
Wang Q, Peng R, Wang J, Li Z, Qu H (2020) NEWLSTM: an optimized long short-term memory language model for sequence prediction. IEEE Access 8:65395–65401 Google Scholar
Shuang K, Li R, Gu M, Loo J, Su S (2020) Major-minor long short-term memory for word-level language model. IEEE Trans Neur Net Lear 31(10):3932–3946 MathSciNet Google Scholar
Kingma DP, Ba J (2014) Adam: A method for stochastic optimization, arXiv: 14126980
Wang B, Lei Y, Li N, Li N (2020) A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Trans Reliab 69(1):401–412 Google Scholar
Zhang H, Zhang Q, Shao S, Niu T, Yang X (2020) Attention-based LSTM network for rotatory machine remaining useful life prediction. IEEE Access 8:132188–132199 Google Scholar
Li X, Zhang W, Ma H, Luo Z, Li X (2020) Data alignments in machinery remaining useful life prediction using deep adversarial neural networks. Knowl-Based Syst 197:105843 Google Scholar