 A comparative study of sound and vibration signals in detection of rotating machine faults using support vector machine and independent component analysisM. Saimurugan and K.I. Ramachandran International Journal of Data Analysis Techniques and Strategies, 2014, vol. 6, issue 2, 188-204 Abstract: In rotating machines, shaft and bearing are the critical components of interest for fault diagnosis. In recent years, the application of machine learning for fault diagnosis is gaining momentum. This paper presents the fault diagnosis of shaft and bearing using support vector machine (SVM). The experiments were conducted by simulating the 12 fault conditions of shaft and bearing. The statistical features were extracted from the collected vibration and sound signals and these features were given as an input to the classifier. The extracted statistical features were subjected to dimensionality reduction using independent component analysis (ICA) and classified using SVM. The obtained results of SVM and SVM with ICA are evaluated. The effectiveness of the vibration and sound signal for the fault diagnosis are discussed and compared. Keywords: rotating machines; fault diagnosis; machine learning; vibration signals; sound signals; support vector machines; SVM; independent component analysis; ICA; shafts; bearings. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:6:y:2014:i:2:p:188-204 Access Statistics for this article More articles in International Journal of Data Analysis Techniques and Strategies from Inderscience Enterprises Ltd |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.