【摘要】 针对在滚动轴承故障检测和诊断中获取的单通道信息不全面、不准确等问题,提出了全矢本征时间尺度分解(ITD)和核主元分析(KPCA)相结合的方法以进行故障检测与诊断。首先采用全矢ITD对正常运行状态下的同源双通道原始样本数据进行信息融合,得到全矢融合后的主振矢数据,并建立KPCA模型,克服了单通道振动信号信息不完整的缺点。然后运用KPCA模型对待测样本数据进行在线监控,当该模型的T2和SPE统计量超过已设定的控制限时,采用全矢Hilbert包络分析提取故障数据的特征频率以进行故障诊断。实验结果表明,该方法既能较好地检测出滚动轴承的运行状态,又能准确有效地诊断故障类型。更多还原

【Abstract】 It proposes a method which combines full vector intrinsic time scale decomposition(ITD)and the kernel principal component analysis(KPCA)for fault detection and diagnosis in view of the problem of incomplete and inaccurate information about single channel information obtained in the detection and diagnosis of rolling bearings. This method firstly uses the full vector ITD to fuse the homologous dual-channel original sample data in normal operation state,obtains the main vibration vector data after full vector fusion to overcome the disadvantages of incomplete single channel vibration signal,and establishes KPCA model. Then,the sample data is monitored online using the KPCA model,when the model’s T2 and SPE statistics exceed the set limit,full vector Hilbert envelope demodulation is carried out to extract the feature frequency of fault data for the fault diagnosis. The experimental results show that the method can not only detect the running state of rolling bearing,but also can identify the fault type accurately and effectively.更多还原