Blind identification of threshold auto-regressive model for machine fault diagnosis

2007年第2卷第1期

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《机械工程前沿（英文）》 >> 2007年第2卷第1期 doi: 10.1007/s11465-007-0007-9

Blind identification of threshold auto-regressive model for machine fault diagnosis

1.Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084, China; 2.Institute of Modern Manufacturing Engineering, Zhejiang University, Hangzhou 310027, China

发布日期： 2007-03-05

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摘要

A blind identification method was developed for the threshold auto-regressive (TAR) model. The method had good identification accuracy and rapid convergence, especially for higher order systems. The proposed method was then combined with the hidden Markov model (HMM) to determine the auto-regressive (AR) coefficients for each interval used for feature extraction, with the HMM as a classifier. The fault diagnoses during the speed-up and speed-down processes for rotating machinery have been successfully completed. The result of the experiment shows that the proposed method is practical and effective.

关键词

speed-up ; classifier ; practical ; extraction ; experiment