A novel bearing fault diagnosis method based on multi-scale transfer symbolic dynamic entropy and support vector machine

基于多尺度迁移符号动力学熵和支持向量机的轴承诊断方法研究

School of Mechatronic Engineering, Xi'an Technological University, Xi'an 710021, China

Received: 27 October 2021

Abstract

In view of the problem that the generalization ability of traditional data-driven fault diagnosis model declines or even fails in mechanical system diagnosis, a fault diagnosis method based on multi-scale transfer symbolic dynamic entropy and support vector machine is proposed based on the idea of transfer learning. Firstly, multi-scale symbolic dynamic entropy is used to extract fault features from measured vibration signals. And then a feature projection technique based on transfer learning is proposed, which reduces the data distribution difference. Secondly, the parameters of the multi-scale transfer symbol dynamic entropy method are optimized to improve the final fault identification rate. Then, the support vector machine can implement the fault identification. Finally, through the test of bearing fault experimental signals, the rolling bearing diagnosis method based on multi-scale transfer symbol dynamic entropy can effectively improve the generalization ability of data-driven model and realize accurate identification of different fault types of rolling bearing under a small number of samples.

摘要

针对传统数据驱动故障诊断模型在机械系统诊断中存在的泛化能力下降甚至失效的问题, 应用迁移学习的思想, 提出了基于多尺度迁移符号动力学熵和支持向量机的故障识别算法。采用多尺度符号动力学熵提取故障特征, 在此基础上提出基于迁移学习的特征映射技术, 使非同分布数据的特征在映射后分布差异减小。对多尺度迁移符号动力学熵方法的参数进行优选, 将其输入支持向量机中, 进一步提高最终的故障识别率。通过轴承故障实验信号的测试证明, 基于多尺度迁移符号动力学熵的滚动轴承诊断方法能够有效提升数据驱动故障诊断模型的泛化能力, 实现少量样本下滚动轴承不同故障位置的准确识别。