本文提出了一种基于小波方差谱熵的故障诊断方法。该方法以信号的方差在小波域上分解形成的谱向量的熵值作为故障的诊断特征，建立了基于小波方差谱熵的故障检测及诊断模型，并提出了基于判别能力因子的小波基选择方法。试验结果验证了小波方差谱熵能有效地反映轴承信号特征并检测出故障类别，实验最后将本文方法同基于小波能谱熵的故障检测方法进行比较，实验结果表明本文建议的方法在检测率及时间上有显著提高。

The new scheme for bearing fault diagnosis based on wavelet variance spectrum entropy is proposed by combining wavelet analysis with entropy theory. The signal variance spectrum entropy in wavelet domain is used as fault diagnosis characteristics. The detection and diagnosis scheme of bearing vibration faults are proposed and the selection method for wavelet base based on discrimination ability factor is also presented. The results show that the wavelet variance spectrum entropy is particularly well adapted to describe bearings fault characteristics and fault diagnosis. The comparison with the method based on wavelet energy spectrum entropy is experimented which demonstrates that the proposed scheme outperforms efficiently other methods in terms of detection time and detection rate.