On‐line reliability estimation for individual components using statistical degradation signal models

With very few exceptions, most contemporary reliability engineering methods are geared towards estimating a population characteristic(s) of a system, subsystem or component. The information so extracted is extremely valuable for manufacturers and others that deal with product in relatively large volumes. In contrast, end users are typically more interested in the behavior of a ‘particular’ component used in their system to arrive at optimal component replacement or maintenance strategies leading to improved system utilization, while reducing risk and maintenance costs. The traditional approach to addressing this need is to monitor the component through degradation signals and ‘classifying’ the state of a component into discrete classes, say ‘good’, ‘bad’ and ‘in‐between’ categories. In the event, one can develop effective degradation signal forecasting models and precisely define component failure in the degradation signal space, then, one can move beyond the classification approach to a more vigorous reliability estimation and forecasting scheme for the individual unit. This paper demonstrates the feasibility of such an approach using ‘general’ polynomial regression models for degradation signal modeling. The proposed methods allow first‐order autocorrelation in the residuals as well as weighted regression. Parametric bootstrap techniques are used for calculating confidence intervals for the estimated reliability. The proposed method is evaluated on a cutting tool monitoring problem. In particular, the method is used to monitor high‐speed steel drill‐bits used for drilling holes in stainless‐steel metal plates. A second study involves modeling and forecasting fatigue‐crack‐growth data from the literature. The task involved estimating and forecasting the reliability of plates expected to fail due to fatigue‐crack‐growth. Both studies reveal very promising results. Copyright © 2002 John Wiley & Sons, Ltd.