Title: A comparative study of sound and vibration signals in detection of rotating machine faults using support vector machine and independent component analysis
Authors: M. Saimurugan; K.I. Ramachandran
Addresses: Department of Mechanical Engineering, Amrita Vishwa Vidyapeetham, Amrita Nagar, Coimbatore, Tamilnadu, 641112, India ' Department of Mechanical Engineering, Amrita Vishwa Vidyapeetham, Amrita Nagar, Coimbatore, Tamilnadu, 641112, India
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.
International Journal of Data Analysis Techniques and Strategies, 2014 Vol.6 No.2, pp.188 - 204
Available online: 08 Jun 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article