Rolling bearing fault diagnosis algorithm using overlapping group sparse-deep complex convolutional neural network

As the key component of a mechanical system, rolling bearings will cause paralysis of the entire mechanical system once they fail. In recent years, considering the high generalization ability and nonlinear modeling ability of deep learning, rolling bearing fault diagnosis methods based on deep learning have been developed, and good results have been achieved. However, because this kind of method is still in the initial development stage, its main problems are as follows. First, it is difficult to extract the composite fault signal feature of rolling bearings. Second, the existing deep learning rolling bearing fault diagnosis methods cannot address the problem of multi-scale information of rolling bearing signals well. Therefore, this paper first proposes the overlapping group sparse model. It constructs weight coefficients by analyzing the salient features of a signal. It uses convex optimization techniques to solve the sparse optimization model and applies the method to extract features of rolling bearing composite faults. For the problem of extracting multi-scale feature information from rolling bearing composite fault signals, this paper proposes a new deep complex convolutional neural network model. This model fully considers the multi-scale information of rolling bearing signals. The complex information in this model not only has a rich representation ability but can also be used to extract more scale information. Finally, the classifier of this model is used to identify rolling bearing faults. This paper proposes a new rolling bearing fault diagnosis algorithm based on overlapping group sparse model-deep complex convolutional neural network. The experimental results show that the method proposed in this paper can not only effectively identify rolling bearing faults under constant operating conditions, but also accurately identify rolling bearing fault signals under changing operating conditions. Additionally, the classification accuracy of the method proposed in this paper is superior to that of traditional machine learning methods. The proposed method also has certain advantages over other deep learning methods.