A multi-scale convolutional neural network for bearing compound fault diagnosis under various noise conditions

Recently, with the urgent demand for data-driven approaches in practical industrial scenarios, the deep learning diagnosis model in noise environments has attracted increasing attention. However, the existing research has two limitations: (1) the complex and changeable environmental noise, which cannot ensure the high-performance diagnosis of the model in different noise domains and (2) the possibility of multiple faults occurring simultaneously, which brings challenges to the model diagnosis. This paper presents a novel anti-noise multi-scale convolutional neural network (AM-CNN) for solving the issue of compound fault diagnosis under different intensity noises. First, we propose a residual pre-processing block according to the principle of noise superposition to process the input information and present the residual loss to construct a new loss function. Additionally, considering the strong coupling of input information, we design a multi-scale convolution block to realize multi-scale feature extraction for enhancing the proposed model's robustness and effectiveness. Finally, a multi-label classifier is utilized to simultaneously distinguish multiple bearing faults. The proposed AM-CNN is verified under our collected compound fault dataset. On average, AM-CNN improves 39.93% accuracy and 25.84% Fl-macro under the no-noise working condition and 45.67% accuracy and 27.72% Fl-macro under different intensity noise working conditions compared with the existing methods. Furthermore, the experimental results show that AM-CNN can achieve good cross-domain performance with 100% accuracy and 100% F1-macro. Thus, AM-CNN has the potential to be an accurate and stable fault diagnosis tool.