Survey of prognostics methods for condition-based maintenance in engineering systems

Abstract

It is not surprising that the idea of efficient maintenance algorithms (originally motivated by strict emission regulations, and now driven by safety issues, logistics and customer satisfaction) has culminated in the socalled condition-based maintenance program. Condition-based program/monitoring consists of two major tasks, i.e., diagnostics and prognostics each of which has provided the impetus and technical challenges to the scientists and engineers in various fields of engineering. Prognostics deals with the prediction of the remaining useful life, future condition, or probability of reliable operation of an equipment based on the acquired condition monitoring data. This approach to modern maintenance practice promises to reduce the downtime, spares inventory, maintenance costs, and safety hazards. Given the significance of prognostics capabilities and the maturity of condition monitoring technology, there have been an increasing number of publications on machinery prognostics in the past few years. These publications cover a wide range of issues important to prognostics. Fortunately, improvement in computational resources technology has come to the aid of engineers by presenting more powerful onboard computational resources to make some aspects of these new problems tractable. In addition, it is possible to even leverage connected vehicle information through cloud-computing. Our goal is to provide a report on the state of the art and to summarize some of the recent advances in prognostics with the emphasis on models, algorithms and technologies used for data processing and decision making.

Optimized Maintenance Decision-Making – A Simulation-supported Prescriptive Analytics Approach based on Probabilistic Cost-Benefit Analysis

“…Figure 1). While diagnostic / prognostic approaches may be conceptualized regarding three levels with varying degrees of complexity, i.e., 1) existing, 2) future failure mode prognostics and 3) post-action prognostics with a focus in the literature on the first (Taheri, Kolmanovsky & Gusikhin, 2019), in a Prescriptive Maintenance setting, typically all three of them are exploited. Accordingly, additional business value is gained from analyzing the impact of predicted failures on operational and maintenance activities, to quantitatively compare decision options and their effects on Key Performance Indicators (KPIs).…”