Remaining Useful Life Prediction Based on Improved Temporal Convolutional Network for Nuclear Power Plant Valves

Proper risk assessment and monitoring of critical component is crucial to the safe operation of Nuclear Power Plants. One of the ways to ensure real-time monitoring is the development of Prognostics and Health Management systems for safety-critical equipment. Recently, the remaining useful life prediction (RUL) has been found to be important in ensuring predictive maintenance and avoiding critical component failure. With the development of artificial intelligent techniques, deep learning algorithms are becoming popular for RUL prediction. Consequently, this paper presents RUL prediction techniques for nuclear plant electric gate valves with a temporal convolution network (TCN). The main advantage of using TCN is its ability to capture and process useful information in short-term sensor measurement changes. Moreover, the efficiency of the proposed TCN is enhanced by incorporating a convolution auto-encoder as a preprocessing layer in its structure, which greatly improved the residual convolution mode. The proposed method is verified on the electric gate valves experimental dataset that represents the real-world operation of the valve, and the result obtained is compared with other conventional data-driven approaches. The evaluation result shows impressive performance of the proposed model in predicting the remaining service life of the gate valves used in the nuclear reactor control system. Moreover, the generalization of the proposed model is evaluated on the turbofan engine benchmark dataset. The evaluation result also shows improved performance in the predicted RUL. Broader application of the proposed TCN is envisaged for critical components in other industries.

[1]  Jacob Bortman,et al.  Contribution of dynamic modeling to prognostics of rotating machinery , 2019, Mechanical Systems and Signal Processing.

[2]  Jie Bai,et al.  A new hyperparameters optimization method for convolutional neural networks , 2019, Pattern Recognit. Lett..

[3]  Noureddine Zerhouni,et al.  Acquisition: From System to Data , 2016 .

[4]  Jing Chen,et al.  Exploring spatial-temporal relations via deep convolutional neural networks for traffic flow prediction with incomplete data , 2019, Appl. Soft Comput..

[5]  Jinsong Yu,et al.  Remaining useful life prediction for engineering systems under dynamic operational conditions: A semi-Markov decision process-based approach , 2019, Chinese Journal of Aeronautics.

[6]  A. C. Cilliers,et al.  A survey of the state of condition-based maintenance (CBM) in the nuclear power industry , 2018 .

[7]  Vladlen Koltun,et al.  An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling , 2018, ArXiv.

[8]  Jamie B. Coble,et al.  A Review of Prognostics and Health Management Applications in Nuclear Power Plants , 2020, International Journal of Prognostics and Health Management.

[9]  Hang Wang,et al.  Remaining useful life prediction techniques for electric valves based on convolution auto encoder and long short term memory. , 2020, ISA transactions.

[10]  Rafael Gouriveau,et al.  Towards Accurate and Reproducible Predictions for Prognostic: an Approach Combining a RRBF Network and an AutoRegressive Model , 2010 .

[11]  Ming Chen,et al.  A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network , 2020 .

[12]  Guanghua Xu,et al.  Health indicator construction of machinery based on end-to-end trainable convolution recurrent neural networks , 2020 .

[13]  P. Lall,et al.  Prognostics and health management of electronics , 2006, 2006 11th International Symposium on Advanced Packaging Materials: Processes, Properties and Interface.

[14]  Paulo Sollero,et al.  Analysis of two-dimensional fatigue crack propagation in thin aluminum plates using the Paris law modified by a closure concept , 2019, Engineering Analysis with Boundary Elements.

[15]  C. T. Papadopoulos,et al.  A classification and review of timed Markov models of manufacturing systems , 2019, Comput. Ind. Eng..

[16]  Kai Goebel,et al.  Bayesian hierarchical model-based prognostics for lithium-ion batteries , 2018, Reliab. Eng. Syst. Saf..

[17]  M. Kulkarni,et al.  Weibull accelerated failure time regression model for remaining useful life prediction of bearing working under multiple operating conditions , 2019 .

[18]  Ranjan Ganguli,et al.  Fuzzy-Logic-Based Health Monitoring and Residual-Life Prediction for Composite Helicopter Rotor , 2007 .

[19]  Emmanuel Ramasso,et al.  Investigating computational geometry for failure prognostics , 2014, International Journal of Prognostics and Health Management.

[20]  Yu Zheng,et al.  An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation , 2020, Comput. Ind..

[21]  Noureddine Zerhouni,et al.  From Prognostics and Health Systems Management to Predictive Maintenance 1: Monitoring and Prognostics , 2016 .

[22]  Changyong Lee,et al.  A similarity based prognostics approach for real time health management of electronics using impedance analysis and SVM regression , 2018, Microelectron. Reliab..

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

[24]  Shouxiang Wang,et al.  A novel smart meter data compression method via stacked convolutional sparse auto-encoder , 2020, International Journal of Electrical Power & Energy Systems.

[25]  Vladlen Koltun,et al.  Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.

[26]  Yaguo Lei,et al.  Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery , 2020, Neurocomputing.

[27]  Khanh T.P. Nguyen,et al.  A new dynamic predictive maintenance framework using deep learning for failure prognostics , 2019, Reliab. Eng. Syst. Saf..

[28]  Jay Lee,et al.  Intelligent prognostics tools and e-maintenance , 2006, Comput. Ind..

[29]  Xianming Huang,et al.  Intelligent remote monitoring and manufacturing system of production line based on industrial Internet of Things , 2020, Comput. Commun..

[30]  Kaixiang Peng,et al.  A deep belief network based health indicator construction and remaining useful life prediction using improved particle filter , 2019, Neurocomputing.

[31]  Yu Zhao,et al.  An improved Wiener process model with adaptive drift and diffusion for online remaining useful life prediction , 2019, Mechanical Systems and Signal Processing.

[32]  Yuefei Wang,et al.  Fault diagnosis of reciprocating compressor valve with the method integrating acoustic emission signal and simulated valve motion , 2015 .

[33]  Yanhui Lin,et al.  Deep diagnostics and prognostics: An integrated hierarchical learning framework in PHM applications , 2018, Appl. Soft Comput..

[34]  Athanasios Kolios,et al.  A Markov chains prognostics framework for complex degradation processes , 2020, Reliab. Eng. Syst. Saf..

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

[36]  Chao Hu,et al.  Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds , 2019, Reliab. Eng. Syst. Saf..