.png){kind=icon}
.png){kind=icon}
Return
A Multiple-Model Reliability Prediction Approach for Condition-Based Maintenance
.png){kind=icon}
PRE
AI
DOI：10.1109/TR.2018.2825470.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Numerous prognostic methods have been developed, aiming at predicting future system reliability with the highest possible accuracy. It is striking that the relation with the subsequent maintenance optimization process is generally overlooked, while it is important in practice. Additionally, almost all existing methods are based on a single degradation measure, and focus on systems with only one degradation and failure mode. In practice, however, multiple degradation measures are often available and needed to adequately predict future system degradation. Moreover, systems may suffer from various kinds of faults, all resulting in different degradation behaviors. To accommodate these properties, we establish a link between failure prognosis and maintenance optimization, and accordingly propose a multivariate multiple-model approach to system reliability prediction. We conclude that in the presence of multiple degradation modes and provided they are correctly identified, a multiple-model approach outperforms a single-model approach with respect to the prediction accuracy. Moreover, in the presence of multiple degradation and failure modes, overall predictions of the remaining useful life as generated by common prognostic approaches are not directly suited for maintenance decision making, as different kinds of system failures and maintenance activities are associated with different costs. In contrast, our approach yields conditional predictions of future system reliability, which much better suit the maintenance optimization process.
Keywords:
Condition-based maintenance
degradation modeling
multivariate time series analysis
prognostics
recursive Bayesian filtering
reliability prediction
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.7
Papers:
2.7K
Citations:
8.5K