基于深度支持证据统计的轴承运行可靠性评估

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振动与冲击 ›› 2022, Vol. 41 ›› Issue (5) : 60-66.

论文

肖文荣1，2，陈法法1，陈保家1

作者信息 +

Bearing operation reliability evaluation based on deep support evidence statistics

XIAO Wenrong1,2, CHEN Fafa1, CHEN Baojia1

Author information +

文章历史 +

摘要

某台具体设备的运行可靠性评估是“个性”问题。本文基于设备运行数据大而丰富的特点，从证据论的观点出发，结合深度卷积自编码器，提出了一种深度支持证据统计方法，以实现无失效样本信息或缺乏分布函数等相关先验知识时的轴承运行可靠性评估。在传统的证据或特征获取过程中需要大量的人工干预或先验知识，利用深度卷积稀疏自编码器以实现证据的自动获取，借助支持向量数据描述的思想，通过统计比较标定与过程证据间的动态变化过程，最终完成某台具体设备的运行可靠性评估。随后通过航空轴承实验对提出的方法进行了验证，结果表明运用该方法得到的可靠度，能较好的反映轴承运行可靠性的“个性”。

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.

导出引用

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[J]. Journal of Vibration and Shock, 2022, 41(5): 60-66

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