Induction machine fault detection using clone selection programming

Authors:
Zhaohui Gan;Ming-Bo Zhao;Tommy W. S. Chow
Affiliations:
Department of Electric Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;School of Physics and Electronics Engineering, Shanxi University, China;Department of Electric Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

A clonal selection programming (CSP)-based fault detection system is developed for performing induction machine fault detection and analysis. Four feature vectors are extracted from power spectra of machine vibration signals. The extracted features are inputs of an CSP-based classifier for fault identification and classification. In this paper, the proposed CSP-based machine fault diagnostic system has been intensively tested with unbalanced electrical faults and mechanical faults operating at different rotating speeds. The proposed system is not only able to detect electrical and mechanical faults correctly, but the rules generated is also very simple and compact and is easy for people to understand, This will be proved to be extremely useful for practical industrial applications.