Abstract
Early detection of slowly varying small faults is an essential step for fault prognosis. In this paper, we first propose an average accumulative (AA) based time varying principal component analysis (PCA) model for early detection of slowly varying faults. The AA based method can increase the fault size as well as decrease the noise energy. Then, designated component analysis (DCA) is introduced for developing an AA-DCA method to diagnose the root cause of the fault, which is helpful for the operator to make maintenance decisions. Combining the advantage of the cumulative sum (CUSUM) based method and the AA based method, a CUSUM-AA based method is developed to detect faults at earlier times. Finally, the remaining useful life (RUL) prediction model with error correction is established by nonlinear fitting. Once online fault size defined by detection statistics is obtained by an early diagnosis algorithm, real-time RUL prediction can be directly estimated without extra recursive regression.
Highlights
Abnormal monitoring and efficient tolerant control of large scale systems is significant and crucial since it can guarantee safety and economic production [1,2,3,4,5,6,7,8,9,10,11,12]
Either abnormal detection or fault pattern recognition can not tell the operator whether a fault will occur and how long will the system be significantly affected, which is the foundation of condition-based maintenance
The purpose of fault prognosis is to judge whether a fault will occur and how long the system will break down in the future, which is the foundation of condition-based maintenance
Summary
Abnormal monitoring and efficient tolerant control of large scale systems is significant and crucial since it can guarantee safety and economic production [1,2,3,4,5,6,7,8,9,10,11,12]. The main innovations of this paper are as follows: since early diagnosis of slowly varying faults is the fundamental basis of RUL prediction, a more effective AA based time variant model for early fault detection is proposed in the first part of the paper, which can accumulate the fault size as well as decrease the noise of the observation. By using this method, variance of the statistics is much smaller, which is more advantageous for combining a AA based method with a CUSUM based method for detecting fault trends earlier.
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