Failure Rate Identification of a Reparable System Governed by Coupled ODE-PDEs and Deep Learning based Implementation

This paper is concerned with the problem of machine failure rate identification of a 3-state reparable system and its implementation via deep learning. The mathematical model is governed by a distributed parameter system involving coupled partial and integro-differential equations. The objective of this work is to identify the failure rates using the sampled system output measurements. Deep learning based failure rate identification methods are proposed. Numerical examples are provided to illustrate the designs and results.