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

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.

Integrated Equipment Health Prognosis Considering Crack Initiation Time Uncertainty and Random Shock

“…In Ref. [4], the degradation model was selected to be exponential. Two error terms were considered: one was treated as a multiplicative random variable, and the other was treated as a multiplicative Brownian motion process.…”

“…Prognostic algorithms have been proposed in a large number of publications for various industrial applications. Those algorithms are mostly either data-driven [3][4][5][6][7], physically motivated [8][9][10][11][12][13][14][15][16] or model and data integrated [17][18][19][20][21][22][23][24][25][26].…”

A condition-based maintenance model for a single component in a system with scheduled and unscheduled downs

“…Lu and Meeker [19]) to determine the optimal control limit and the monitored interval in terms of cost, downtime and reliability. Gebraeel et al [9,10] extended the general degradation model to estimate the RUL distribution from sensor signals by a Wiener process and Bayesian updating. Using this technique, a single-unit replacement problem is formulated as a Markov decision process to develop a structured replacement policy in Elwany et al [8].…”

Nonlinear degradation modeling and maintenance policy for a two-stage degradation system based on cumulative damage model

“…However, the degradation process of some systems present two-stage feature due to the influences of internal mechanism and external environment etc [7], where the degradation rate is suddenly increased. For example, the vibration-based degradation signals of bearings [8] and vibration signals special frequency band energy of gearboxes [14] exhibit two-stage characteristic in degradation test. There are two typical models with independent random increment, continuous time model [7] and cumulative damage model [21], that can be used to present system degradation process.…”