Remaining Useful Life Estimation of Bearings Based on Nonlinear Dimensional Reduction Combined with Timing Signals

In data-driven prognostic methods, the prediction accuracy of the estimation for remaining useful life of bearings mainly depends on the performance of health indicators, which are usually fused some statistical features extracted from vibrating signals. However, the existing health indicators have the following two drawbacks: (1) The differnet ranges of the statistical features have the different contributions to construct the health indicators, the expert knowledge is required to extract the features. (2) When convolutional neural networks are utilized to tackle time-frequency features of signals, the time-series of signals are not considered. To overcome these drawbacks, in this study, the method combining convolutional neural network with gated recurrent unit is proposed to extract the time-frequency image features. The extracted features are utilized to construct health indicator and predict remaining useful life of bearings. First, original signals are converted into time-frequency images by using continuous wavelet transform so as to form the original feature sets. Second, with convolutional and pooling layers of convolutional neural networks, the most sensitive features of time-frequency images are selected from the original feature sets. Finally, these selected features are fed into the gated recurrent unit to construct the health indicator. The results state that the proposed method shows the enhance performance than the related studies which have used the same bearing dataset provided by PRONOSTIA. Keywords—continuous wavelet transform, convolution neural network, gated recurrent unit, health indicators, remaining useful life.

[1]  Noureddine Zerhouni,et al.  Remaining useful life estimation based on nonlinear feature reduction and support vector regression , 2013, Eng. Appl. Artif. Intell..

[2]  Guanghua Xu,et al.  Rotating speed isolation and its application to rolling element bearing fault diagnosis under large speed variation conditions , 2015 .

[3]  Fulei Chu,et al.  Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography , 2004 .

[4]  Xiaoli Li,et al.  Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life , 2016, DASFAA.

[5]  Jie Liu,et al.  An Enhanced Diagnostic Scheme for Bearing Condition Monitoring , 2010, IEEE Transactions on Instrumentation and Measurement.

[6]  Hong-Hee Lee,et al.  Probabilistic frequency-domain discrete wavelet transform for better detection of bearing faults in induction motors , 2016, Neurocomputing.

[7]  Yaguo Lei,et al.  Condition monitoring and fault diagnosis of planetary gearboxes: A review , 2014 .

[8]  Kamran Javed,et al.  A robust and reliable data-driven prognostics approach based on Extreme Learning Machine and Fuzzy Clustering , 2014 .

[9]  Enrico Zio,et al.  Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method , 2014, Digit. Signal Process..

[10]  Jake Bouvrie,et al.  Notes on Convolutional Neural Networks , 2006 .

[11]  B. Tang,et al.  Bearing remaining useful life estimation based on time–frequency representation and supervised dimensionality reduction , 2016 .

[12]  E. Voges,et al.  Digital Single Sideband Detection For Interferometric Sensors , 1984, Other Conferences.

[13]  Liang Guo,et al.  A recurrent neural network based health indicator for remaining useful life prediction of bearings , 2017, Neurocomputing.

[14]  Peter Sarlin,et al.  Self-organizing time map: An abstraction of temporal multivariate patterns , 2012, Neurocomputing.

[15]  Mohamed Tkiouat,et al.  Rolling element bearing remaining useful life estimation based on a convolutional long-short-term memory network , 2018 .

[16]  Michael Pecht,et al.  Physics-of-failure-based prognostics for electronic products , 2009 .

[17]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[18]  Tommy W. S. Chow,et al.  Anomaly Detection and Fault Prognosis for Bearings , 2016, IEEE Transactions on Instrumentation and Measurement.

[19]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[20]  Changqing Shen,et al.  An equivalent cyclic energy indicator for bearing performance degradation assessment , 2016 .

[21]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[22]  Yaguo Lei,et al.  A Model-Based Method for Remaining Useful Life Prediction of Machinery , 2016, IEEE Transactions on Reliability.

[23]  Wojciech Zaremba,et al.  An Empirical Exploration of Recurrent Network Architectures , 2015, ICML.

[24]  Robert X. Gao,et al.  A multi-time scale approach to remaining useful life prediction in rolling bearing , 2017 .

[25]  F.O. Heimes,et al.  Recurrent neural networks for remaining useful life estimation , 2008, 2008 International Conference on Prognostics and Health Management.