基于信息融合和广义循环互相关熵的电机轴承故障诊断

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摘要针对传统基于单路振动信号的故障识别可靠性较差和传统谱相关方法难以有效处理非高斯噪声的问题，提出了一种基于多传感器振动信号信息融合和广义循环互相关熵谱的轴承故障诊断方法。首先推导了广义互相关熵、广义循环互相关熵和广义循环互相关熵谱密度的计算公式；然后给出了电机轴承故障诊断步骤；再利用轴承外圈故障仿真信号，分析了轴承故障振动信号的频谱特征，验证了广义相关熵的降噪性能，表明广义循环相关熵能有效处理高斯和非高斯噪声。最后将两路振动信号通过广义循环互相关熵进行融合，并应用于电机轴承故障诊断，实验结果表明：广义循环互相关熵能有效提取电机轴承内圈、外圈局部裂纹故障频谱特征，提高了故障诊断的准确性和可靠性，其性能优于传统的谱相关方法。

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	李辉1
	郝如江2

关键词 ：广义互相关熵, 广义循环互相关熵函数, 广义循环互相关熵谱密度, 循环平稳信号, 轴承, 故障诊断

Abstract：In the field of fault diagnosis and identification, the reliability of fault feature extraction and recognition based on single sensor vibration signal is lower. The traditional spectral correlation density method has poor time-frequency aggregation ability. In order to solve these problems, a bearing fault diagnosis method based on multiple sensors information fusion and generalized cyclic cross correntropy spectrum density is proposed. Firstly, the definition and calculation formula of generalized cross correntropy, generalized cyclic cross correntropy and generalized cyclic cross correntropy spectral density are deduced in detail. Then the de-noising performance of generalized cyclic cross correntropy is analyzed and verified by means of a simulative bearing outer race defect signal, which shows that generalized cyclic cross correntropy can effectively deal with Gaussian and non Gaussian noise. Finally, the generalized cyclic cross correntropy is applied to the fault detection and identification of the motor bearing inner and outer race defect. The experimental results show that the generalized cyclic cross correntropy can effectively extract the fault characteristics of the rolling bearing, and the generalized cyclic cross correntropy can accurately describe the spectral characteristics of the bearing defect. The performance of generalized cyclic cross correntropy spectral density is better than that of the traditional spectral correlation density method.

Key words： Generalized cross correntropy Generalized cyclic cross correntropy function Generalized cyclic cross correntropy spectral density function Cyclostationary signals Bearing Fault diagnosis

收稿日期: 2020-08-05 出版日期: 2022-01-28

引用本文:

李辉1，郝如江2. 基于信息融合和广义循环互相关熵的电机轴承故障诊断[J]. 振动与冲击, 2022, 41(2): 200-207.
LI Hui1,HAO Rujiang2. Rolling bearing fault diagnosis based on sensor information fusion and generalized cyclic cross correntropy spectrum density. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(2): 200-207.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I2/200

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