Remaining useful life estimation for PEMFC based on monitoring data

Proton exchange membrane fuel cell (PEMFC) is a new type of clean and efficient fuel cell with wonderful prospect. In consideration of the key position of power supply in the device, estimating remaining useful life (RUL) of PEMFC has positive effects in its new stages of development. Currently, RUL estimation of industrial facilities has played an extremely important role in the field of high safety and reliability requirements. For practical systems, the state is often a stochastic process with inevitable degradation during the runtime. In truth, a large amount of data about system operation state is available, and the linear stochastic degradation model is widely applicable to stochastic degradation system. In view of this, this paper selects the proton exchange membrane fuel cell as an object of study, and the remaining useful life is estimated based on its monitoring data. At first, a brief introduction to proton exchange membrane fuel cell is covered. Then, the stochastic degradation model is built and the method of predicting the remaining life is given. At last, the RUL predicted value is obtained and the feasibility of the method is verified by the simulation results with high precision.

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

[2]  Donghua Zhou,et al.  A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution , 2013, Eur. J. Oper. Res..

[3]  Zhou Dong,et al.  A Survey on Anomaly Detection, Life Prediction and Maintenance Decision for Industrial Processes , 2013 .

[4]  Noureddine Zerhouni,et al.  Prognostics of PEM fuel cell in a particle filtering framework , 2014 .

[5]  Noureddine Zerhouni,et al.  Remaining Useful Life Estimation of Critical Components With Application to Bearings , 2012, IEEE Transactions on Reliability.

[6]  M.G. Pecht,et al.  Prognostics and health management of electronics , 2008, IEEE Transactions on Components and Packaging Technologies.

[7]  L. Carrette,et al.  Fuel Cells - Fundamentals and Applications , 2001 .

[8]  Viral S. Mehta,et al.  Review and analysis of PEM fuel cell design and manufacturing , 2003 .

[9]  Benoît Iung,et al.  Remaining useful life estimation based on stochastic deterioration models: A comparative study , 2013, Reliab. Eng. Syst. Saf..

[10]  Tony L. Schmitz,et al.  Prediction of remaining useful life for fatigue-damaged structures using Bayesian inference , 2012 .

[11]  G. A. Whitmore,et al.  Threshold Regression for Survival Analysis: Modeling Event Times by a Stochastic Process Reaching a Boundary , 2006, 0708.0346.

[12]  Noureddine Zerhouni,et al.  Improving accuracy of long-term prognostics of PEMFC stack to estimate remaining useful life , 2015, 2015 IEEE International Conference on Industrial Technology (ICIT).

[13]  Pierluigi Pisu,et al.  An Unscented Kalman Filter Based Approach for the Health-Monitoring and Prognostics of a Polymer Electrolyte Membrane Fuel Cell , 2012 .

[14]  Bhaskar Saha,et al.  Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.

[15]  Xiao-Sheng Si,et al.  A Survey on Anomaly Detection, Life Prediction and Maintenance Decision for Industrial Processes: A Survey on Anomaly Detection, Life Prediction and Maintenance Decision for Industrial Processes , 2014 .

[16]  Noureddine Zerhouni,et al.  Degradations analysis and aging modeling for health assessment and prognostics of PEMFC , 2016, Reliab. Eng. Syst. Saf..

[17]  R. Gouriveau,et al.  Fuel Cells prognostics using echo state network , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.