DCA-Based Real-Time Residual Useful Life Prediction for Critical Faulty Component

Funa Zhou,Yulin Gao,Jiayu Wang

doi:10.1155/2017/8492139

Abstract

Residual useful life (RUL) prediction is significant for condition-based maintenance. Traditional data-driven RUL prediction method can only predict fault trend of the system rather than RUL of a specific system component. Thus it cannot tell the operator which component should be maintained. The innovation of this paper is as follows: (1) Wavelet filtering based method is developed for early detection of slowly varying fault. (2) Designated component analysis is introduced as a feature extraction tool to define the fault precursor of a specific component. (3) Exponential life prediction model is established by nonlinear fitting of the historical RUL and the fault size characterized by the statistics used. Once online detection statistics is obtained, real-time RUL of the critical component can be predicted online. Simulation shows the effectiveness of this algorithm.

Highlights

With the rapid development of modern industrial technology, reliability, maintainability, and security of largescale system have widely received attention [1,2,3,4]
Root cause identification, and Residual useful life (RUL) prediction are the stages for efficient system monitoring
After establishing principal component analysis (PCA) modeling under normal operating conditions, multivariate statistics called squared prediction error (SPE), can be used for fault detection and diagnosis

Summary

Introduction

With the rapid development of modern industrial technology, reliability, maintainability, and security of largescale system have widely received attention [1,2,3,4]. Existing method for establishing the fault damage precursor can be categorized into 2 classes: data-driven method and model-based method. Thanks to the rapid development of sensor technology and condition monitoring technology, large amount of observation data reflecting status of the system can be used to establish damage precursor by using data-driven method [9, 13]. Fault size computed by fault reconstruction can be used to establish a prediction model based on exponential smoothing technique. These methods share the same deficiency that fault direction is computed by PCA related method [27]. DCA is introduced as a feature extraction tool for establishing fault damage precursor and life prediction model. Conclusions and further research are given in the last section of the paper

Review of PCA and DCA

Fault Damage Precursor Based on Historical Observation

Simulation

Conclusions