Transducer invariant multi-class fault classification in a rotor-bearing system using support vector machines

• Published in: Measurement : journal of the International Measurement Confederation Vol. 58; pp. 363 - 374
• Main Authors: Fatima, S., Guduri, B., Mohanty, A.R., Naikan, V.N.A.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2014
• Subjects: Bearing; Fault classification; Speed; Unbalance; Vibration; Unbalance; Fault classification; Speed; Vibration; Bearing
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2014.08.042

•Fault classification for bearings and rotor unbalance at five speeds.•Time domain features have only been used for fault classification by SVM.•SVM classification has been shown to be independent of transducer location. Faults in a rotor-bearing system due to bearings and unbalance have been classified using support vector machines (SVMs). Vibration signals on a rotor-bearing system were measured simultaneously at five different rotating speeds using seven transducers. The most sensitive feature of the vibration signals has been determined using compensation distance evaluation technique. Multi-class SVMs classification algorithm was then implemented for classification of the faults by considering SVMs created by the possible combinations of the most two sensitive features for each type of fault. By using optimal SVM parameters, the effective location of transducer among seven transducers for best classification of the faults has been investigated and found that any transducer provides a classification of 75% or better and this classification rate increases when more transducers are considered. This paper provides a robust SVM based technique using only time domain data without any additional preprocessing for classifying bearing and unbalance faults.