A New Degradation Indicator Based on a Statistical Anomaly Approach

作者：Pordeus Gomes Joao Paulo; Harrop Galvao Roberto Kawakami; Yoneyama Takashi; Leao Bruno P

来源：IEEE Transactions on Reliability, 2016, 65(1): 326-335.

DOI：10.1109/TR.2015.2417171

• Fault detection
• multivariate statistical process control
• nonparametric methods

This paper presents a new method for combining measured parameters into a single indicator for monitoring the condition of systems subjected to degradation effects. The proposed approach integrates the use of nonparametric density estimation techniques into Runger's method, which allows for the separation of variables that are directly related to degradation effects from those which are not. Two simulated case studies are presented for illustration, namely, the monitoring of a flap extension and retraction system, and a gas turbine employed as an auxiliary power unit. For comparison, degradation indicators are also calculated by using Hotteling's and Runger's methods, as well as a nonparametric method without separation of variables. In both case studies, the proposed method provided the best results in terms of fault detection performance and suitability for remaining useful life prediction.

• 出版日期2016-3