Classical mechanics-inspired optimization metaheuristic for induction machines bearing failures detection and diagnosis

This paper deals with induction machines bearing failures detection and diagnosis using vibration and temperature signals. It proposes the use of a new Classical Mechanics-inspired Optimization (CMO) metaheuristic for data clustering. To ensure failure detection, transitions from a state to another...

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Bibliographic Details
Published inIECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society pp. 3803 - 3808
Main Authors Khamoudj, Charaf Eddine, Benbouzid-Si Tayeb, Fatima, Benatchba, Karima, Benbouzid, Mohamed
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2017
Subjects
bearing failure
Classical Mechanics-inspired Optimization (CMO) metaheuristic
detection
diagnosis
Extraterrestrial measurements
Force
Induction machine
Induction machines
Planets
Satellites
Temperature measurement
Vibrations
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Summary:This paper deals with induction machines bearing failures detection and diagnosis using vibration and temperature signals. It proposes the use of a new Classical Mechanics-inspired Optimization (CMO) metaheuristic for data clustering. To ensure failure detection, transitions from a state to another is analyzed in order to form a transitional model between system states generated by the clustering. The performances of the proposed new metaheuristic are evaluated on the PRONOSTIA experimental platform data.
DOI:10.1109/IECON.2017.8216649