Bearing fault detection and identification using relational data clustering with composite differential evolution optimization

Research output: Contribution to journalArticlepeer-review

Abstract

Bearing faults in machinery are among the most critical faults that require attention by maintenance personnel at early stages of fault initiation. In many cases it is difficult to directly and accurately identify the fault type and its extent under varying operating conditions. This work demonstrates a novel procedure for bearing fault detection and identification in an experimental set-up. Three seeded faults, in the rotating machinery supported by the test ball bearing, include inner race fault, outer race fault and one roller fault. The rotor is run at different speeds and with small level of rotating mass unbalance. Accelerometer based vibration signals are analysed for the different bearing faults' signatures using statistical features, frequency spectra and wavelet coefficients. The composite differential evolution technique is proposed for parameter estimation when the system response is known a-priori. The algorithm is compared to five other differential evolution algorithms using conventional crossover and mutation operators. The objective is to correlate bearing faults to the extracted vibration features. The results of this analysis will be extended for applications in real time bearing condition monitoring system.

Original languageEnglish (US)
Pages (from-to)57-61
Number of pages5
JournalInternational Journal of COMADEM
Volume23
Issue number3
StatePublished - Jun 30 2020

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Signal Processing
  • Safety, Risk, Reliability and Quality
  • Strategy and Management
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Bearing fault detection and identification using relational data clustering with composite differential evolution optimization'. Together they form a unique fingerprint.

Cite this