Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines

This paper studies the application of support vector machines (SVMs) to the detection and classification of rolling-element bearing faults. The training of the SVMs is carried out using the sequential minimal optimization (SMO) algorithm. In this paper, a mechanism for selecting adequate training parameters is proposed. This proposal makes the classification procedure fast and effective. Various scenarios are examined using two sets of vibration data, and the results are compared with those available in the literature that are relevant to this investigation.