The gearbox is an important component in the mechanical transmission system and plays a key role in aerospace, wind power and other fields. Gear failure is one of the main causes of gearbox failure, and therefore it is very important to accurately diagnose the type of gear failure under different operating conditions. Aiming at the problem that it is difficult to effectively identify the fault types of gears using traditional methods under complex and changeable working conditions, a fault diagnosis method based on multi-sensor information fusion and Visual Geometry Group (VGG) is proposed. First, the power spectral density is calculated with the raw frequency domain signal collected by multiple sensors before being transformed into a power spectral density energy map after information fusion. Second, the obtained energy map is combined with VGG to obtain the fault diagnosis model of the gear. Finally, two datasets are used to verify the effectiveness and generalization ability of the method. The experimental results show that the accuracy of the method can reach 100% at most on both datasets.

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In order to solve the problem of AGV fault detection system’s complexion and low accuracy, a convolutional neural network (CNN) based on the status monitoring and fault diagnosis method for automatic guided vehicle (AGV) is proposed. Firstly, the vibration signals of the core components of AGV are converted into two-dimensional (2D) images. Secondly, 2D images are input into convolution neural network for training. Finally, the trained model is used to monitor the running status of AGV and identify faults. The results show that the proposed method can effectively monitor the status of AGV in operation.