Multiple Fault Diagnosis - Traditional mechanical and electrical fault diagnosis models for high-voltage circuit breakers (HVCBs) encounter the following problems: the recognition accuracy is low, and the overfitting phenomenon of the model is serious, making its generalization ability poor. To overcome above problems, this paper proposed a new diagnosis model of HVCBs based on the multi-sensor information fusion and the multi-depth neural networks (MultiDNN). This approach used fifteen typical time-domain features extracted from signals of exciting coil current and angular displacement to indicate the operational state of HVCBs, and combined the multiple deep neural networks (DNN) to improve the accuracy and standard deviation. Six operational states were simulated based on the experimental platform, including normal state, two typical mechanical faults and four typical electrical faults, and the coil current and angular displacement signals are collected in each state to verify the effectiveness of the proposed model. The experimental results showed that, compared with the traditional fault diagnosis model, the Multi-DNN based on multi-sensor information fusion can be applied to finding a better equilibrium between underfitting and overfitting phenomenon of the model.