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.

Additional Metadata
Keywords Machine learning, Modeling, Particle filtering, Predictive maintenance, Time to Failure (TTF)
Persistent URL dx.doi.org/10.1007/s10489-017-1083-0
Journal Applied Intelligence
Citation
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