Abstract
This study proposes a new intelligent diagnosis method for rotating machinery using support vector machines (SVMs) and possibility theory on basis of the vibration signals, to detect faults and identify fault types at an early stage. The non-dimensional symptom parameters (NSPs) in the frequency domain are defined to reflect the features of the vibration signals. SVMs are used to construct the optimal classification hyper-plane, then to integrate a new symptom parameter, which is called as synthetic symptom parameter (SSP). Finally, the possibility distributions of the SSP are used to distinguish faults by sequential inference and possibility theory. The proposed method has been applied to detect the faults of the V-belt driving equipment in a centrifugal fan, and the efficiency of the method has been verified using practical examples.
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Xue, H., Li, K., Wang, H., Chen, P. (2013). Sequential Diagnosis Method for Rotating Machinery Using Support Vector Machines and Possibility Theory. In: Huang, DS., Bevilacqua, V., Figueroa, J.C., Premaratne, P. (eds) Intelligent Computing Theories. ICIC 2013. Lecture Notes in Computer Science, vol 7995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39479-9_37
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DOI: https://doi.org/10.1007/978-3-642-39479-9_37
Publisher Name: Springer, Berlin, Heidelberg
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