Particle filtering-based methods for time to failure estimation with a real-world prognostic application
One of core technologies for prognostics is to predict failures before they occur and estimate time to failure (TTF) by using built-in predictive models. The predictive model could be either physics-based model or machine learning-based model. Machine learning-based predictive modeling is an emerging application of machine learning to machinery maintenance. Accurate TTF estimation could help performing predictive action “just-in-time”. However, the developed predictive models sometimes fail to provide a precise TTF estimate. To address this issue, we propose a Particle Filtering (PF)-based method to estimate TTF. After introducing the PF-based algorithm, we present the implementation along with the experimental results obtained from a case study of Auxiliary Power Unit (APU) prognostics. To our best knowledge, this is the first application of PF-based method to APU prognostic. The results demonstrated that the PF-based method is useful for estimating TTF for predictive maintenance and it greatly improved TTF estimation precision for APU prognostics.
|Keywords||Machine learning, Modeling, Particle filtering, Predictive maintenance, Time to Failure (TTF)|
Yang, C. (Chunsheng), Lou, Q. (Qingfeng), Liu, J, Yang, Y. (Yubin), & Cheng, Q. (Qiangqiang). (2017). Particle filtering-based methods for time to failure estimation with a real-world prognostic application. Applied Intelligence, 1–11. doi:10.1007/s10489-017-1083-0