基于DSmT与小波网络的齿轮箱早期故障融合诊断

针对齿轮箱早期故障特征十分微弱难以有效辨识问题，提出基于DSmT理论与小波神经网络的齿轮箱早期故障融合诊断模型。利用多个振动传感器合理布置在齿轮箱的多个关键部位采集多源振动信息并进行特征提取；利用多个并联小波神经网络实现齿轮箱早期故障的初级诊断获得彼此独立的多个证据；利用DSmT理论对多个独立证据进行融合决策得出齿轮箱的最终诊断结论。DSmT理论克服了传统DST证据理论的局限性，小波神经网络实现多源证据信度分配的客观化。诊断实验结果表明，该方法能有效提高齿轮箱早期故障特征的辨识精度、降低诊断的不确定性。

Gearbox incipient fault fusion diagnosis based on dsmt theory and wavelet neural network

Aimed at gearbox incipient fault features are very weak and these features are difficult to be distinguished, a diagnosis model based on DSmT theory and wavelet neural network for gearbox incipient fault is proposed. Firstly, multiple vibration sensors are reasonably arranged on gearbox critical position to collect multi-source vibration information for feature extraction. Several shunt-wound wavelet networks are used to carry on primary fault diagnosis and acquire independent evidences each other. Then, DSmT theory is used to combine different independent evidences and got the final decision result. The DSmT theory overcome the shortcoming of the traditional DST theory, the wavelet neural network realized the objectivity of multi-source evidence belief assignment. The diagnostic tests show that this method can effectively improve the identification accuracy of gearbox incipient fault features and reduce diagnostic uncertainty.