注：HJ十三五预研项目（No. 3020801010203）
作者：赵天升¹，常雪²
单位：1.中国船舶重工集团公司第七一三研究所，郑州 450015；
2.重庆大学机械传动国家重点实验室，重庆 400044
中图分类号：TH212；TH213.3            
文献标识码：A       
文章编号：1006-883X(2021)02-0027-07
收稿日期：2020-12-28

摘要：针对强背景噪声下滚动轴承早期故障信号信噪比低、特征提取难度大的问题，提出一种将自回归-最小熵解卷积（autoregressive-minimum entropy deconvolution，AR-MED）与Teager能量算子（teager energy operator，TEO）相结合的滚动轴承故障诊断方法。为了达到增强故障信号中冲击成分的目的，采用AR-MED对信号进行滤波处理。依据滤波后信号的Teager能量谱，获取滚动轴承的故障特征频率。通过对仿真信号和实测信号进行分析，验证了该文所提方法在强背景噪声下滚动轴承早期故障诊断中的有效性。
关键词：滚动轴承；故障诊断；自回归-最小熵解卷积；Teager能量算子

Early Fault Diagnosis Method of Rolling Bearing Based on AR-MED and TEO
ZHAO Tiansheng¹, CHANG Xue²
1. The 713 Research Institute of CSIC, Zhengzhou 450015, China; 2. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
Abstract: Aiming at the problem that the early fault signal of the rolling bearing has low signal-to-noise ratio and difficult feature extraction under strong background noise, the paper proposes a fault diagnosis method for rolling bearing combining autoregressive-minimum entropy deconvolution and teager energy operator. In order to achieve the purpose of enhancing the impact component in the fault signal, the signal is preprocessed using AR-MED. According to the teager energy spectrum of the filtered signal, the fault characteristic frequency of the rolling bearing is obtained. By analyzing the simulated signal and the measured signal, the effectiveness of the proposed method in detecting early faults of rolling bearings under strong background noise is verified.
Key words: rolling bearing; fault diagnosis; autoregressive-minimum entropy deconvolution; teager energy operator

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备注：2021年 第27卷 第02期