Multiscale Kernal Based Convolutional Neural Networks for Gear Fault Diagnosis Under Variable Operation Conditions

To enhance the reliability and safety of industrial systems, gear fault diagnosis in manipulators is crucial. However, the operation of manipulators often occurs under variable working conditions, resulting in vibration signals with characteristics such as nonlinearity, non-stationarity, and strong environmental noise. Additionally, studying vibration signals traditionally necessitates the use of extra vibration sensors, thereby increasing operational costs for the manipulator. In response to these challenges, this paper introduces a multiscale kernel convolutional neural network (MKCNN) that relies on three-phase stator current data for effective gear fault diagnosis. First, Z-score normalization is used to preprocess the data to enhance the training efficiency of the network model for current data under complex working conditions. Then, a convolutional neural network architecture with multi-scale convolution kernels was constructed to extract multi-level sensory information through convolution kernels of different scales to improve the feature extraction capabilities of the model. Additionally, an attention mechanism module is introduced, assigning higher weights to features in proximity to feature frequencies. Finally, to validate the effectiveness of the proposed method, current signals are collected from a real manipulator gear test platform. Experimental results show that MKCNN can fully extract multi-resolution information and has a high accuracy in gear fault detection, which is higher than other comparison methods.