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In the work process of mining machinery gears, vibration signals are not only influenced by friction, nonlinear stiffness and non-stationary load, but also influenced by strong noise. How to extract the fault feature information effectively, identify the fault status accurately and eliminate the uncertainty in the identification process is the key to evaluate the fault status in strong noise. A new gear fault diagnosis method in strong noise is proposed based on multi-sensor information fusion, which combines wavelet correlation feature scale entropy (WCFSE), self-organizing feature map (SOM) neural network and Dempster-Shafer (D-S) evidence theory algorithm. The noise is reduced by the way of a wavelet transform correlation filter, to calculate the Shannon entropy of denoised wavelet coefficients, which can reflect the vibration signal complexity. The WCFSE of standard training samples defined as the input vectors of an SOM neural network is used to train the neural network, and the gear status clusters in a competitive layer. In order to improve the accuracy and completeness in the identification process, multi-sensor fusion technology is introduced to establish the recognition framework of D-S evidence theory and the basic belief function allocation method based on the recognition rate of statistical SOM neural network. Each sensor can provide sub-evidence, and the gear fault diagnosis is analyzed according to the combination rules and basic belief function. The experimental results show that the proposed gear diagnosis method in strong noise can identify the gear fault accurately, eliminate the identified uncertainty and imperfection with a single sensor. The recognition rates of two-sensor fusion beyond 80%, and the recognition rates of three-sensor fusion beyond 88%, especially the status with tooth loss beyond 95%. The fault recognition rates have been greatly improved compared with a single sensor, so this is an effective method of gear fault diagnosis in strong noise.

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