Residual-life distributions from component degradation signals: A Bayesian approach

TLDR

Real‑time condition monitoring collects sensor data to assess device health and, when modeled, enables residual‑life distribution estimation for maintenance decisions. The study develops Bayesian updating methods to characterize device degradation signals and update exponential degradation model parameters using real‑time monitoring data. Bayesian updating of exponential degradation model parameters from real‑time monitoring data yields a closed‑form residual‑life distribution, which we apply to accelerated‑testing bearing degradation signals.

Abstract

Real-time condition monitoring is becoming an important tool in maintenance decision-making. Condition monitoring is the process of collecting real-time sensor information from a functioning device in order to reason about the health of the device. To make effective use of condition information, it is useful to characterize a device degradation signal, a quantity computed from condition information that captures the current state of the device and provides information on how that condition is likely to evolve in the future. If properly modeled, the degradation signal can be used to compute a residual-life distribution for the device being monitored, which can then be used in decision models. In this work, we develop Bayesian updating methods that use real-time condition monitoring information to update the stochastic parameters of exponential degradation models. We use these degradation models to develop a closed-form residual-life distribution for the monitored device. Finally, we apply these degradation and residual-life models to degradation signals obtained through the accelerated testing of bearings.