Abstract
A mathematical morphology based status feature extraction method for rolling bearing is proposed in this paper. A status recognition method for bearing based on the feature is given under multiple load conditions. The experiment results verify that the proposed method can perform better than support vector machine based on time-frequency domain features, and also predict bearing status precisely with a mean accuracy of 99.53%. The comparison results show the proposed method can eliminate the influence of load conditions, and distinguish the actual status of bearing accurately. Above all, the calculation of the proposed method is very simple.
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Ge, Y., Guo, L., Dou, Y. (2019). A Fault Diagnosis Method Based on Mathematical Morphology for Bearing Under Multiple Load Conditions. In: Wang, K., Wang, Y., Strandhagen, J., Yu, T. (eds) Advanced Manufacturing and Automation VIII. IWAMA 2018. Lecture Notes in Electrical Engineering, vol 484. Springer, Singapore. https://doi.org/10.1007/978-981-13-2375-1_3
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DOI: https://doi.org/10.1007/978-981-13-2375-1_3
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