PDF and Breakdown Time Prediction for Unobservable Wear Using Enhanced Particle Filters in Precognitive Maintenance

Machine health prognosis is crucial to reduce unexpected downtime, maintenance costs, and safety hazards in industrial systems. In this paper, a novel methodology to predict probability density function (pdf) and breakdown time of unobservable degradation processes is proposed. A transition-based autoregressive moving average model and an enhanced particle filter (EPF) are developed at the prognosis stage for the pdf prediction of industrial wear. The strictly monotonic increasing behavior of degradation is ensured by executing a monotonic resampling scheme in EPF, and the number of particles is chosen to be time-varying to reduce computation costs. The effectiveness of our proposed framework is tested on the tool wear in an industrial milling machine, and achieves the predicted bounds with accuracies of at least 90.3% as well as saves more than 50% calculation time without loss of accuracy.