On-line performance assessment and fault diagnosis of mechanical systems

Shang-Liang Chen, Yin Ting Cheng, Hsien Cheng Liu, Yun Yao Chen

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This study integrates sensors, signal capture equipment, industrial computers and machinery health checkup software to develop an On-line Performance Assessment and Fault Diagnosis of Mechanical System, helping engineers predict mechanical conditions. Physical quantities captured by the sensors is utilized to process physical signals, and the Wavelet Packet Energy method is used for the feature extraction of non-stationary signals in coordination with the Principal Component Analysis for feature selection. This study establishes On-line Performance Assessment and Fault Diagnosis of Mechanical System based on Discriminant Analysis which is able to immediately determine the mechanical performance. When abnormal mechanical conditions occur, Bayesian Network will be activated to construct error diagnostic model and determine possible causes of error or malfunction of the machinery. Finally, the system is applied to the fan motor, high-speed spindle motor and AC motor of the machine tool. Experimental results show that the theory can effectively diagnose mechanical performance remarkable with an accuracy rate of 92.50% or higher.

Original languageEnglish
Pages (from-to)705-715
Number of pages11
JournalTransactions of the Canadian Society for Mechanical Engineering
Volume39
Issue number3
Publication statusPublished - 2015 Jan 1

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering

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