Abstract
An accurate determination of the system failure threshold is an essential requirement in achieving an appropriate system residual life prediction and a reasonable planned maintenance strategy optimization afterward for degradation systems. This paper proposes a failure threshold determination method based on quantitative measurement of the similarity between the operating system and the historical systems. The similarity is formulated by a weighted average function and then calculated by a convex quadratic formulation to minimizing the variance between the operating system and the historical systems. With an accurate determination of the system failure threshold in real-time, a better prediction of the residual life for the operating system is achieved. Finally, a real case study for several power-shift steering transmission systems monitored using oil spectral analysis is adopted to illustrate and numerically compare the improved performance of the proposed method.
Highlights
Degradation-induced failure is an inevitable and natural phenomenon for various industrial devices and systems
This paper proposes a similarity-based failure threshold determination method for the system residual life prediction
The similarity between the operating system and historical systems is measured by a weighted average function and calculated by using a convex quadratic formulation, and the corresponding system failure threshold can be determined
Summary
Degradation-induced failure is an inevitable and natural phenomenon for various industrial devices and systems. The work in [29] presents a system failure threshold determination method based on the statistic characteristics of the last degradation data collections from multiple historical systems, and the uncertainty in the failure threshold distribution is considered. Based on the proposed method, the failure threshold can be determined for an operating system with the collected real-time degradation information This is of practical significance to attaining a more reasonable and accurate RL distribution estimation and, is the main contribution of this paper.
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