1.The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China；
2. College of Mechanical & Electrical Engineering, Henan University of Technology, Zhengzhou, 450007, China
摘要依据小波变换带通滤波特性和相关分析提出一种滚动轴承故障特征提取新方法。针对带通滤波器参数难以快速自适应选取的问题，提出利用局域均值分解(Local Mean Decomposition，LMD)所得乘积函数(production function，PF)的统计特征快速设定滤波器中心频率，通过分析滤波信号小波系数谱改进Shannon熵与滤波器带宽参数间的关系给出滤波器带宽参数优化策略。对仿真信号和内外圈故障轴承信号的分析结果表明，该方法能自适应优化小波滤波器参数，有效提取滚动轴承冲击性故障特征。
Abstract：A new method based on the filter characteristics of wavelet transform and autocorrelation analysis is proposed for feature extraction form rolling bearing vibration signal. Aiming at the wavelet parameter optimization problem, local mean decomposition is used to produce appropriate production functions (PFs), and the center frequency of the wavelet filter is then adaptively and efficiently determined using the PFs which takes advantage of the statistical information contained within them. The bandwidth of the wavelet filter is optimized according to the relationship between the modified Shannon entropy of the filtered signal and the filter bandwidth. The analysis results of the experimental signal and rolling bearing vibration signal with inner-race and outer-race faults show that the filter parameters can be optimized adaptively, and the fault feature of rolling bearing can be extracted by the proposed method.
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