Feature extraction of vibration signals based on empirical wavelet transform
WANG Xi1,2, TIAN Muqin1,2, SONG Jiancheng1,2, HE Ying1,2, FENG Junling1,2, LIN Lingyan1,2
1.Nationaland Local Joint Engineering Laboratory of Mining Intelligent Electrical Apparatus Technology,Taiyuan University of Technology,Taiyuan 030024,China；
2.Shanxi Key Laboratory of Mining Electrical Equipmentand Intelligent Control,Taiyuan University of Technology,Taiyuan 030024,China
In order to solve the problem of difficulty in identifying the maneuvering load of rock tunnel driving, a method for extracting the feature vector of the vibration signal of the head of the roadheader based on the combination of empirical wavelet transform (EWT) and correlation threshold denoising was proposed.First, the vibration signals of the heading machine under different rock wall hardness are converted into several component signals after EWT processing; thereafter, the correlation threshold denoising method is used to denoise each component of the vibration signal; finally, the correlation coefficient of each component and the original signal under different rock wall hardness is calculated.According to the selected threshold, the components containing more vibration information are extracted, and the vibration signal feature vector is constructed, so as to realize the feature vector extraction of the vibration signal of the cutting head of the roadheader.Simulation experiments show that EWT can effectively extract the feature vector of the vibration signal of the cutting head of the roadheader under different rock wall hardness, and its performance is better than the singular value decomposition feature quantity extraction method.
王茜，田慕琴，宋建成，贺颖，冯君玲，吝伶艳. 基于经验小波变换的振动信号特征量提取[J]. 振动与冲击, 2021, 40(16): 261-266.
WANG Xi, TIAN Muqin, SONG Jiancheng,HE Ying, FENG Junling, LIN Lingyan. Feature extraction of vibration signals based on empirical wavelet transform. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(16): 261-266.
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