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A platform for research: civil engineering, architecture and urbanism
Detection and classification of rolling-element bearing faults using support vector machines
This paper proposes development of support vector machines (SVMs) for 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.
Detection and classification of rolling-element bearing faults using support vector machines
This paper proposes development of support vector machines (SVMs) for 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.
Detection and classification of rolling-element bearing faults using support vector machines
Rojas, A. (author) / Nandi, A.K. (author)
2005
6 Seiten, 4 Quellen
Conference paper
English
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