A generalized remaining useful life prediction method for complex systems based on composite health indicator

Abstract As one of the key techniques in Prognostics and Health Management (PHM), accurate Remaining Useful Life (RUL) prediction can effectively reduce the number of downtime maintenance and significantly improve economic benefits. In this paper, a generalized RUL prediction method is proposed for complex systems with multiple Condition Monitoring (CM) signals. A stochastic degradation model is proposed to characterize the system degradation behavior, based on which the respective reliability characteristics such as the RUL and its Confidence Interval (CI) are explicitly derived. Considering the degradation model, two desirable properties of the Health Indicator (HI) are put forward and their respective quantitative evaluation methods are developed. With the desirable properties, a nonlinear data fusion method based on Genetic Programming (GP) is proposed to construct a superior composite HI. In this way, the multiple CM signals are fused to provide a better prediction capability. Finally, the proposed integrated methodology is validated on the C-MAPSS data set of aircraft turbine engines.

[1]  David,et al.  Abrupt fault remaining useful life estimation using measurements from a reciprocating compressor valve failure , 2019, Mechanical Systems and Signal Processing.

[2]  Yongbo Li,et al.  Health Condition Monitoring and Early Fault Diagnosis of Bearings Using SDF and Intrinsic Characteristic-Scale Decomposition , 2016, IEEE Transactions on Instrumentation and Measurement.

[3]  Shi Jianming,et al.  An ensemble model for engineered systems prognostics combining health index synthesis approach and particle filtering , 2017 .

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

[5]  Shuai Huang,et al.  Integration of Data Fusion Methodology and Degradation Modeling Process to Improve Prognostics , 2016, IEEE Transactions on Automation Science and Engineering.

[6]  Jianbo Yu,et al.  Tool condition prognostics using logistic regression with penalization and manifold regularization , 2018, Appl. Soft Comput..

[7]  Abhinav Saxena,et al.  Damage propagation modeling for aircraft engine run-to-failure simulation , 2008, 2008 International Conference on Prognostics and Health Management.

[8]  Ye Tao,et al.  A novel health indicator for on-line lithium-ion batteries remaining useful life prediction , 2016 .

[9]  Haidong Shao,et al.  Rolling bearing health prognosis using a modified health index based hierarchical gated recurrent unit network , 2019, Mechanism and Machine Theory.

[10]  Enrico Zio,et al.  A belief function theory based approach to combining different representation of uncertainty in prognostics , 2015, Inf. Sci..

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

[12]  Ying Chen,et al.  A Physics-Based Modeling Approach for Performance Monitoring in Gas Turbine Engines , 2015, IEEE Transactions on Reliability.

[13]  Piero Baraldi,et al.  Differential evolution-based multi-objective optimization for the definition of a health indicator for fault diagnostics and prognostics , 2018 .

[14]  Nagi Gebraeel,et al.  Scalable prognostic models for large-scale condition monitoring applications , 2017 .

[15]  Enrico Zio,et al.  Degradation state mining and identification for railway point machines , 2019, Reliab. Eng. Syst. Saf..

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

[17]  Sandeep Kumar,et al.  A novel soft computing method for engine RUL prediction , 2017, Multimedia Tools and Applications.

[18]  Jie Chen,et al.  Performance degradation assessment of a wind turbine gearbox based on multi-sensor data fusion , 2019, Mechanism and Machine Theory.

[19]  Qing He,et al.  Prediction of Railcar Remaining Useful Life by Multiple Data Source Fusion , 2015, IEEE Transactions on Intelligent Transportation Systems.

[20]  Joseph Mathew,et al.  Bearing fault prognosis based on health state probability estimation , 2012, Expert Syst. Appl..

[21]  Theodoros H. Loutas,et al.  Structural health monitoring data fusion for in-situ life prognosis of composite structures , 2018, Reliab. Eng. Syst. Saf..

[22]  Jinquan Huang,et al.  Reduced kernel recursive least squares algorithm for aero-engine degradation prediction , 2017 .

[23]  Đani Juričić,et al.  Bearing fault prognostics using Rényi entropy based features and Gaussian process models , 2015 .

[24]  Rongjing Hong,et al.  HYGP-MSAM based model for slewing bearing residual useful life prediction , 2019, Measurement.

[25]  Brigitte Chebel-Morello,et al.  Remaining useful life estimation based on discriminating shapelet extraction , 2015, Reliab. Eng. Syst. Saf..

[26]  Zhenpo Wang,et al.  Prognostic health condition for lithium battery using the partial incremental capacity and Gaussian process regression , 2019, Journal of Power Sources.

[27]  Chao Hu,et al.  Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life , 2011, 2011 IEEE Conference on Prognostics and Health Management.

[28]  Jianjun Shi,et al.  A Data-Level Fusion Model for Developing Composite Health Indices for Degradation Modeling and Prognostic Analysis , 2013, IEEE Transactions on Automation Science and Engineering.

[29]  Xiaofeng Hu,et al.  Remaining useful life prediction based on health index similarity , 2019, Reliab. Eng. Syst. Saf..

[30]  Michael Pecht,et al.  Application of a state space modeling technique to system prognostics based on a health index for condition-based maintenance , 2012 .

[31]  Hongwen He,et al.  Validation and verification of a hybrid method for remaining useful life prediction of lithium-ion batteries , 2019, Journal of Cleaner Production.

[32]  Abdallah Chehade,et al.  Optimize the Signal Quality of the Composite Health  Index via Data Fusion for Degradation Modeling  and Prognostic Analysis , 2017, IEEE Transactions on Automation Science and Engineering.

[33]  Chuan Lyu,et al.  A novel health indicator for PEMFC state of health estimation and remaining useful life prediction , 2017 .

[34]  E. Zio,et al.  An ensemble of models for integrating dependent sources of information for the prognosis of the remaining useful life of Proton Exchange Membrane Fuel Cells , 2019, Mechanical Systems and Signal Processing.

[35]  Yaguo Lei,et al.  A New Method Based on Stochastic Process Models for Machine Remaining Useful Life Prediction , 2016, IEEE Transactions on Instrumentation and Measurement.

[36]  Jian Sun,et al.  A novel method based upon modified composite spectrum and relative entropy for degradation feature extraction of hydraulic pump , 2019 .

[37]  Qian Liu,et al.  Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process , 2019, Reliab. Eng. Syst. Saf..

[38]  Kay Chen Tan,et al.  Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics , 2017, IEEE Transactions on Neural Networks and Learning Systems.

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

[40]  Peter W. Tse,et al.  A multi-sensor approach to remaining useful life estimation for a slurry pump , 2019 .

[41]  Xi Zhang,et al.  Integration of Data-Level Fusion Model and Kernel Methods for Degradation Modeling and Prognostic Analysis , 2018, IEEE Transactions on Reliability.

[42]  Amir Asif,et al.  A multimodal and hybrid deep neural network model for Remaining Useful Life estimation , 2019, Comput. Ind..

[43]  Nagi Gebraeel,et al.  Sensory-Updated Residual Life Distributions for Components With Exponential Degradation Patterns , 2006, IEEE Transactions on Automation Science and Engineering.

[44]  C. Joseph Lu,et al.  Using Degradation Measures to Estimate a Time-to-Failure Distribution , 1993 .

[45]  Bo-Suk Yang,et al.  Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine , 2012, WCE 2010.

[46]  Yu Peng,et al.  A modified echo state network based remaining useful life estimation approach , 2012, 2012 IEEE Conference on Prognostics and Health Management.

[47]  Linxia Liao,et al.  Discovering Prognostic Features Using Genetic Programming in Remaining Useful Life Prediction , 2014, IEEE Transactions on Industrial Electronics.

[48]  Kaibo Liu,et al.  Statistical degradation modeling and prognostics of multiple sensor signals via data fusion: A composite health index approach , 2018 .

[49]  Abdallah Chehade,et al.  Sensory-Based Failure Threshold Estimation for Remaining Useful Life Prediction , 2017, IEEE Transactions on Reliability.

[50]  Kay Chen Tan,et al.  A Novel Time Series-Histogram of Features (TS-HoF) Method for Prognostic Applications , 2018, IEEE Transactions on Emerging Topics in Computational Intelligence.

[51]  Yong Zhang,et al.  A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction method , 2019, Measurement.

[52]  Kaibo Liu,et al.  A Generic Health Index Approach for Multisensor Degradation Modeling and Sensor Selection , 2019, IEEE Transactions on Automation Science and Engineering.

[53]  Noureddine Zerhouni,et al.  Railway Point Machine Prognostics Based on Feature Fusion and Health State Assessment , 2019, IEEE Transactions on Instrumentation and Measurement.

[54]  Huihui Miao,et al.  Joint Learning of Degradation Assessment and RUL Prediction for Aeroengines via Dual-Task Deep LSTM Networks , 2019, IEEE Transactions on Industrial Informatics.

[55]  Nagi Gebraeel,et al.  Multistream sensor fusion-based prognostics model for systems with single failure modes , 2017, Reliab. Eng. Syst. Saf..