Health indicator construction and remaining useful life prediction for space Stirling cryocooler

Stirling cryocoolers are widely used to refrigerate significant facilities in military and aerospace applications. However, under the influences of high-frequency piston motion and thermal environment deterioration, the refrigerating performance of Stirling cryocoolers will worsen inevitably, thus affecting the successful accomplishment of space mission. In this article, a methodology on assessing the performance of space Stirling cryocoolers is proposed, which involves the analysis of the failure mechanism, health indicator construction and remaining useful life prediction of the cryocooler. The potential factors affecting the refrigerating performance are discussed first. In view of these, three health indicators representing the degradation process of cryocoolers are constructed and then a multi-indicator method based on particle filter is proposed for remaining useful life prediction. Finally, the proposed method is validated by a Stirling cryocooler from one retired aircraft, and the results show that the constructed health indicators and remaining useful life prediciton approaches are effective for performance assessment of Stirling cryocooler.

[1]  Yinong Wu,et al.  The lifetime prediction model of stirling cryocooler for infrared detector assembly , 2013, Other Conferences.

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

[3]  Yong-Ju Hong,et al.  The effect of operating parameters in the Stirling cryocooler , 2002 .

[4]  Donghua Zhou,et al.  Remaining useful life estimation - A review on the statistical data driven approaches , 2011, Eur. J. Oper. Res..

[5]  Jie Liu,et al.  A multi-step predictor with a variable input pattern for system state forecasting , 2009 .

[6]  Chaochao Chen,et al.  Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach , 2012 .

[7]  W. Wang,et al.  A data-model-fusion prognostic framework for dynamic system state forecasting , 2012, Eng. Appl. Artif. Intell..

[8]  Joseph Mathew,et al.  A review on prognostic techniques for non-stationary and non-linear rotating systems , 2015 .

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

[10]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[11]  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.

[12]  Kevin MacG. Adams Availability, Operability, and Testability , 2015 .

[13]  Enrico Zio,et al.  Particle filtering prognostic estimation of the remaining useful life of nonlinear components , 2011, Reliab. Eng. Syst. Saf..

[14]  Matteo Corbetta,et al.  Real-Time Prognosis of Crack Growth Evolution Using Sequential Monte Carlo Methods and Statistical Model Parameters , 2015, IEEE Transactions on Reliability.

[15]  Shaohua Yang,et al.  Performance degradation of space Stirling cryocoolers due to gas contamination , 2011, Applied Optics and Photonics China.

[16]  B.A. Kelley,et al.  System testability analyses in the Space Station Freedom program , 1990, 9th IEEE/AIAA/NASA Conference on Digital Avionics Systems.

[17]  M. Pecht,et al.  rognostics of lithium-ion batteries based on Dempster – Shafer theory and the ayesian Monte Carlo method , 2011 .

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

[19]  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.

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

[21]  Liang Tang,et al.  Risk Measures for Particle-Filtering-Based State-of-Charge Prognosis in Lithium-Ion Batteries , 2013, IEEE Transactions on Industrial Electronics.

[22]  Wei Liang,et al.  Remaining useful life prediction of lithium-ion battery with unscented particle filter technique , 2013, Microelectron. Reliab..

[24]  Hongwen He,et al.  Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries , 2018, IEEE Transactions on Vehicular Technology.

[25]  Andrew Kusiak,et al.  Prognosis of the Remaining Useful Life of Bearings in a Wind Turbine Gearbox , 2016 .

[26]  Yinong Wu,et al.  Failure analysis of the space Stirling cryocoolers , 2011, The Proceedings of 2011 9th International Conference on Reliability, Maintainability and Safety.

[27]  Miaohua Huang,et al.  Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model , 2016, Microelectron. Reliab..