摘要:针对基于小波能量谱和能量谱熵的故障诊断方法要求小波分解系数基本符合高斯分布这一不足,提出一种基于多尺度小波域隐马尔可夫模型(WHMM)参数特征的故障诊断方法。该方法分析了信号多尺度小波分解系数的统计特征,利用隐马尔可夫模型描述小波变换域系数在尺度间,尺度内的统计相关性。采用最大似然估计方法确定的模型参数作为信号特征实现故障诊断。试验结果证实了设计思想的正确性和算法的高效检测性能。最后从小波基、窗口宽度和分类器三个层面对建议方法诊断性能的影响进行分析,结果表明本文方法具有很强的稳定性和鲁棒性。
ABSTRACT: In order to avoid the practical problems that traditional wavelet energy spectrum as the characteristics is insufficient for the diagnosis infer in bearings fault detection, a novel fault detection method based on the wavelet histogram signatures which capture all the first order statistics using a model based on hidden Markov model (HMM) is presented. In this approach, the statistics features of the multi-scale wavelet coefficients generated by the wavelet decomposition of the signals are analyzed, the detail wavelet histogram of the bearing vibration signals can be modeled by the hidden Markov model. The parameters of this model as diagnosis features are introduced to completely describe the wavelet coefficients’ first-order statistics. The scale and shape parameters of the model are estimated by the maximum likelihood method. Comparison of the performance of detection of the proposed approach with the method based on the wavelet energy spectrum and wavelet energy spectrum entropy is experimented. The results show the relative effectiveness of the introduced feature sets in the detection of the bearing conditions with some concluding remarks. The effects of the wavelet base selection, window width and classifier on the proposed method are conducted in the experiments, which evaluate the stability and robustness of the proposed method.