A neural-network-based proportional hazard model for IoT signal fusion and failure prediction

Abstract

Accurate prediction of Remaining Useful Life (RUL) plays a critical role in optimizing condition-based maintenance decisions. In this article, a novel joint prognostic modeling framework that simultaneously combines both time-to-event data and multi-sensor degradation signals is proposed. With the increasing use of IoT devices, unprecedented amounts of diverse signals associated with the underlying health condition of in-situ units have become easily accessible. To take full advantage of the modern IoT-enabled engineering systems, we propose a specialized framework for RUL prediction at the level of individual units. Specifically, a Bayesian linear regression model is developed for the multi-sensor degradation signals and a functional neural network is proposed to allow the proportional hazard model to characterize the complex nonlinearity between the hazard function and degradation signals. Based on the proposed model, an online model updating procedure is established to accurately predict RUL in real time. The advantageous features of the proposed method are demonstrated through simulation studies and the application to a high-fidelity gas turbine engine dataset.