针对滚动轴承这种非平稳振动信号采用的小波包分解的方法来检测故障的存在，运用神经网络来实现故障的分类，还结合D-S理论融合了多个传感器的诊断结果，提高了故障诊断的准确性并通过实验仿真证实。(This course's job is to use the wavelet packet decomposition method for non-stationary vibration signals of rolling bearings to detect the presence of faults and to use nerves. The network is used to classify faults, and the DS results are combined with the diagnostic results of multiple sensors to improve the accuracy of fault diagnosis and confirm it through experimental simulation.)