Abstract：Operation reliability assessment of the specified equipment is an “individual problem”. In this paper, based on the characteristics of large and rich equipment operation data, from the perspective of evidence theory, combined with deep convolution auto encoder, a deep support evidence statistics method is proposed, which can be used to evaluate the operational reliability of mechanical equipment without prior knowledge such as failure sample information or distribution function. In the traditional feature acquisition process, a lot of manual intervention or prior knowledge is needed. Deep Sparse Auto Encoder (DSAE) is used to achieve automatic evidence acquisition, based on the idea of support vector data description (SVDD), the dynamic change process between calibration and process evidence is compared statistically, and finally the operation reliability evaluation of a specific equipment is completed. The reliability of aviation bearing is verified by the test results, which show that the reliability obtained by this method can better reflect the “individual character” of bearing.
肖文荣1，2，陈法法1，陈保家1. 基于深度支持证据统计的轴承运行可靠性评估[J]. 振动与冲击, 2022, 41(5): 60-66.
XIAO Wenrong1,2, CHEN Fafa1, CHEN Baojia1. Bearing operation reliability evaluation based on deep support evidence statistics. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(5): 60-66.
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