PERFORMANCE MONITORING AND FAULT PREDICTION USING A LINEAR PREDICTIVE CODING ALGORITHM

被引:3
|
作者
DORAISWAMI, R
机构
[1] Department of Electrical Engineering, University of New Brunswick, Fredericton
关键词
ARTIFICIAL INTELLIGENCE; SUPERVISORY CONTROL; FAILURE DETECTION; IDENTIFICATION;
D O I
10.1016/0005-1098(93)90111-6
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A systematic and unified approach to monitor performance and to predict fault is proposed based on a robust Linear Predictive Coding Algorithm (LPCA) implemented as part of an expert system. An Auto-Regressive and Moving Average (ARMA) model of the measured output is estimated in real-time and the model estimate is used to monitor performance and predict faults. The expert system is comprised of a knowledge-base which is represented in the form of frames and rules, and the rules are fired in the order of decreasing importance and increasing computation. The proposed scheme is evaluated both on simulated and physical control systems.
引用
收藏
页码:1115 / 1120
页数:6
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