Abstract
The rolling element bearings are widely used in mechanical transmission systems, whose failures are the most frequently encountered factors for machine breakdown. To effectively prevent the unexpected breakdown, it is important to extract the more efficient features to identify the bearings faults. The spectrum analysis for the bearing ball and inner fault recognition may not be suitable in this case. This paper proposed a threshold optimized zero-crossing feature extraction method, which calculates the Euclidean distance feature vector of the rolling bearing states. The optimizing observation window length and intervals number can be obtained, and the optimized feature vector is selected to establish the identification model. Experiment validates the effectiveness of the proposed method.
The work was supported by National Natural Science Foundation of China (No. 51265018, No. 51465022) and the Yunnan Province Personal Training Project (Grant No. KKSY201401096).
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Chen, Q., Wu, X., Liu, T., Li, H. (2018). Zero-Crossing Feature Extraction Based on Threshold Optimization for Rolling Element Bearing. In: Zhu, Q., Na, J., Wu, X. (eds) Innovative Techniques and Applications of Modelling, Identification and Control. Lecture Notes in Electrical Engineering, vol 467. Springer, Singapore. https://doi.org/10.1007/978-981-10-7212-3_26
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DOI: https://doi.org/10.1007/978-981-10-7212-3_26
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