A novel multi-scale competitive network for fault diagnosis in rotating machinery

Abstract

Bearing fault diagnosis plays a vital role in ensuring the safe and reliable operation of rotating machinery. The diagnostic process is more difficult when the fault is in its early stages, as fewer fault components are contained in the vibration signal, making the diagnosis process more difficult. To improve the accuracy and efficiency of fault type identification, a novel multi-scale competitive network for fault diagnosis is proposed in this paper. First, to obtain multi-scale features and fully utilize the features in the intermediate layer, ensuring the completeness of fault information, a novel improved multi-scale feature fusion residual network (IMSFFRN) is proposed to exploit deep features for vibration signals. Specifically, multi-scale features are obtained by convolution with different dilation rates, and features from adjacent intermediate layers are selected for efficient fusion. Second, different features have varying importance in fault detection tasks. To make neurons more sensitive to specific faults, we propose a multiple-winning consciousness self-organizing map (MCSOM) competition layer, in which each neuron learns specific faults through competition, and the neuron that wins the competition updates its weights. This distinguishes the sensitivity of neurons to different faults. Finally, the generalizability of the network is improved by using support vector machines (SVMs) to classify fault classes. To affirm the efficacy of the proposed approach, a comprehensive evaluation is conducted on the CWRU, PU bearing dataset and SEU gear dataset. The results indicate that the accuracies achieved by the method proposed in this paper are 100%, 99.56% and 100%, respectively. It is superior to other methods proposed in this paper. Furthermore, it is observed that the proposed method exhibits high robustness in noisy environments.