Prediction of remaining useful life by data augmentation technique based on dynamic time warping

Abstract In the data-driven approaches for engineering prognostics, the lack of run-to-fail (RTF) data has been one of the bottlenecks that hinders practical applications in the field. In order to tackle this issue, the dynamic time warping (DTW) method is presented to augment the RTF data obtained from different operating conditions or systems to the current system, which plays the role of virtual RTF data under the current condition. Once the virtual RTF data are available, they are used to train a neural network model to predict the remaining useful life of the current system. When multiple RTF data are available with different behavior under different failure modes, an RMSE-based performance criterion is proposed that can adaptively choose the closest match to the current data and use it as the virtual RTF data during the prognosis process. Numerical examples are given to show that the proposed DTW-based data augmentation can predict the RUL with less uncertainty than the conventional neural network model without data mapping.

[1]  L. Peel,et al.  Data driven prognostics using a Kalman filter ensemble of neural network models , 2008, 2008 International Conference on Prognostics and Health Management.

[2]  Pierre Gançarski,et al.  A global averaging method for dynamic time warping, with applications to clustering , 2011, Pattern Recognit..

[3]  Petra Hermann,et al.  Resting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping , 2017, Front. Neurosci..

[4]  Takashi Hiyama,et al.  Predicting remaining useful life of rotating machinery based artificial neural network , 2010, Comput. Math. Appl..

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

[6]  Chetan Gupta,et al.  Long Short-Term Memory Network for Remaining Useful Life estimation , 2017, 2017 IEEE International Conference on Prognostics and Health Management (ICPHM).

[7]  Linxia Liao,et al.  Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction , 2014, IEEE Transactions on Reliability.

[8]  Zhigang Tian,et al.  A neural network approach for remaining useful life prediction utilizing both failure and suspension histories , 2010 .

[9]  Dawn An,et al.  Practical options for selecting data-driven or physics-based prognostics algorithms with reviews , 2015, Reliab. Eng. Syst. Saf..

[10]  P. C. Paris,et al.  A Critical Analysis of Crack Propagation Laws , 1963 .

[11]  A. Massi Pavan,et al.  On-line fault detection of a fuel rod temperature measurement sensor in a nuclear reactor core using ANNs , 2015 .

[12]  Laifa Tao,et al.  Similarity recognition of online data curves based on dynamic spatial time warping for the estimation of lithium-ion battery capacity , 2015 .

[13]  Wesley W. Chu,et al.  An index-based approach for similarity search supporting time warping in large sequence databases , 2001, Proceedings 17th International Conference on Data Engineering.

[14]  Yaguo Lei,et al.  Machinery health prognostics: A systematic review from data acquisition to RUL prediction , 2018 .

[15]  Byeng D. Youn,et al.  A co-training-based approach for prediction of remaining useful life utilizing both failure and suspension data , 2015 .

[16]  Renmin Han,et al.  An accurate and rapid continuous wavelet dynamic time warping algorithm for end‐to‐end mapping in ultra‐long nanopore sequencing , 2018, Bioinform..

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

[18]  Mike Nicolai,et al.  Simulation-driven machine learning: Bearing fault classification , 2018 .

[19]  Eamonn J. Keogh,et al.  Exact indexing of dynamic time warping , 2002, Knowledge and Information Systems.

[20]  Jan Lundberg,et al.  Remaining useful life prediction of grinding mill liners using an artificial neural network , 2013 .

[21]  Christos Faloutsos,et al.  Efficient retrieval of similar time sequences under time warping , 1998, Proceedings 14th International Conference on Data Engineering.

[22]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[23]  F. Camci,et al.  Failure diagnostics for railway point machines using expert systems , 2009, 2009 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives.

[24]  Michael G. Pecht,et al.  A prognostics and health management roadmap for information and electronics-rich systems , 2010, Microelectron. Reliab..

[25]  Nam H. Kim,et al.  Prediction of remaining useful life under different conditions using accelerated life testing data , 2018, Journal of Mechanical Science and Technology.

[26]  Weicheng Cui,et al.  An engineering model of fatigue crack growth under variable amplitude loading , 2008 .

[27]  W. Stefansky.  Rejecting Outliers by Maximum Normed Residual , 1971 .

[28]  Tangbin Xia,et al.  Recent advances in prognostics and health management for advanced manufacturing paradigms , 2018, Reliab. Eng. Syst. Saf..

[29]  Jay Lee,et al.  Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .

[30]  Yu Peng,et al.  Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression , 2013, Microelectron. Reliab..

[31]  Dawn An,et al.  Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab , 2013, Reliab. Eng. Syst. Saf..

[32]  Pradeep Lall,et al.  Prognostics Health Management of Electronic Systems Under Mechanical Shock and Vibration Using Kalman Filter Models and Metrics , 2012, IEEE Transactions on Industrial Electronics.

[33]  Xavier Rodet,et al.  Globally Optimal Short-Time Dynamic Time Warping, Application to Score to Audio Alignment , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[34]  Bo-Suk Yang,et al.  Application of relevance vector machine and survival probability to machine degradation assessment , 2011, Expert Syst. Appl..