基于TSA-WT-SVD的汽车转向助力泵转子裂纹声发射检测研究

Research on Acoustic Emission Detection of Cracks in Automotive Power Steering Pump Rotors Based on TSA-WT-SVD

中文关键词:  声发射  裂纹缺陷  域同步平均技术  小波分解  奇异值分解  马氏判别

英文关键词:acoustic emission  crack defect  time domain synchronous averaging  wavelet decomposition  singular value decomposition  Mahalanobis discrimination

中文摘要:

      针对现有磁粉探伤在汽车转向助力泵转子裂纹检测方面存在主观性强和漏检率高的问题，提出一种基于声发射（Acoustic Emission,AE）技术的转子裂纹缺陷在线无损检测方法。首先，在同一型号的双作用叶片泵的定子内先后装配带有裂纹缺陷和无缺陷的2种转子，在液压试验台上以1 000 r/min和14 MPa的加载参数进行满载试验，并且在泵体上的4个不同位置放置AE传感器，信号采样速率设置为3 MHz。然后，针对2种转子下采集的AE信号，分别按照100 ms时长从每个通道的原始信号中任意截取20个片段。针对每个AE片段，先采用时域同步平均（Time Domain Synchronous Averaging,TSA）算法提高信噪比，接着进行5层小波分解以获取AE信号在不同频段上的特征波形，再对5个细节信号组成的矩阵进行奇异值分解（Singular Value Decomposition,SVD），并以前5个奇异值作为标准特征。最后，针对转子有缺陷和无缺陷这2种情况，分别计算每个AE片段的奇异值向量与标准特征矩阵的马氏距离，并以最小距离作为分类依据。研究结果表明，当AE传感器布置在出油口附近时，所提出的TSA-WT-SVD方法辨识转子裂纹的准确率在95%以上。

英文摘要:

      Aiming at the problems of strong subjectivity and high missed detection rate in the current magnetic particle inspection of cracks in the rotor of automotive power steering pump,a rotor crack defect online non-destructive testing method based on acoustic emission（AE）technology is proposed.Firstly,two types of rotors with crack defects and no defects are assembled in the stator of the same model of double acting vane pump,and a full load test is conducted on a hydraulic test bench with loading parameters of 1 000r/min and 14 MPa.AE sensors are placed at four different positions on the pump body,and the signal sampling rate is set to 3 MHz.Then,for the AE signals collected under two types of rotors,20 segments are randomly selected from the original signals of each channel for a duration of 100 ms.For each AE segment,the time domain synchronous average(TSA) algorithm is first used to improve the signal-to-noise ratio.Then,a 5-layer wavelet decomposition is performed to obtain the characteristic waveforms of the AE signal in different frequency bands.The matrix composed of 5 detail signals is then subjected to singular value decomposition,with the first 5 singular values used as standard features.Finally,for the rotor with defects and no defects and flawless rotors,the Mahalanobis distance between the singular value vector of each AE segment and the standard feature matrix is calculated,and the minimum distance is used as the classification basis.The research results indicate that when the AE sensor is placed near the oil outlet,the accuracy of the TSA-WT-SVD method proposed in identifying rotor cracks is over 95%.