Abstract
This paper presents a new approach to intelligent fault diagnosis of the machinery based on granular computing. The tolerance granularity space mode is constructed by means of the inner-class distance defined in the attributes space. Different features of the vibration signals, including time domain statistical features and frequency domain statistical features, are extracted and selected using distance evaluation technique as the attributes to construct the granular structure. Finally, the proposed approach is applied to fault diagnosis of locomotive bearings, testing results show that the proposed approach can reliably recognize different faulty categories and severities.
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Zhousuo, Z., Xiaoxu, Y., Wei, C. (2009). Intelligent Technique and Its Application in Fault Diagnosis of Locomotive Bearing Based on Granular Computing. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_81
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DOI: https://doi.org/10.1007/978-3-642-01513-7_81
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