Bearing fault diagnosis in high noise environment using multi-scale processing, channel-attention and feature-enhanced convolutional neural network model

This paper presents a model using deep learning techniques which includes Multi-scale processing, Channel attention, Feature enhancement, and anomaly Classification layers, referred to as MCFCNN, for bearing fault diagnosis in noisy industrial environments. The MCFCNN network combines multi-channel parallel convolution, effectively capturing spatial information, and introduces channel attention mechanisms to adaptively recalibrate channel-level feature responses. Secondary neurons are introduced to enhance the model’s ability to capture complex nonlinear patterns related to bearing faults. The model was tested and compared to other models using a publicly available data set. In a simulated high-noise environment, the proposed model outperforms existing models in fault diagnosis, with accuracy greater than 80% even at high signal-to-noise (SNR) ratio. At SNR = -6, the MCFCNN records higher accuracy (83%), precision (89%), and recall rates (84.5%) as compared to prior models. The proposed model can be integrated into the maintenance management system to enhance bearing health assessment and prediction, improving machine prognostics.