A Support Vector Machine approach based on physical model training for rolling element bearing fault detection in industrial environments

A hybrid two stage one-against-all Support Vector Machine (SVM) approach is proposed for the automated diagnosis of defective rolling element bearings. The basic concept and major advantage of the method, is that its training can be performed using simulation data, which result from a well established model, describing the dynamic response of defective rolling element bearings. Then, vibration measurements, resulting from the machine under condition monitoring, can be imported and processed directly by the already trained SVM, eliminating thus the need of training the SVM with experimental data of the specific defective bearing. A key aspect of the method is the data preprocessing approach, which among others, includes order analysis, in order to overcome problems related to sudden changes of the shaft rotating speed. Moreover, frequency domain features both from the raw signal as well as from the demodulated signal are used as inputs to the SVM classifiers for a two-stage recognition and classification procedure. At the first stage, a SVM classifier separates the normal condition signals from the faulty signals. At the second stage, a SVM classifier recognizes and categorizes the type of the fault. The effectiveness of the method tested in one literature established experimental test case and in three different industrial test cases, including a total number of 34 measurements. Each test case includes successive measurements from bearings under different types of defects, different loads and different rotation speeds. In all cases, the method presents 100% classification success. (C) 2011 Elsevier Ltd. All rights reserved.