Global contextual feature aggregation networks with multiscale attention mechanism for mechanical fault diagnosis under non-stationary conditions

In recent years, the rapid development of convolutional neural networks (CNNs) has significantly advanced the progress of intelligent fault diagnosis. Most currently-available CNN-based diagnostic models are built on the premise that the monitored machine operates under stable conditions. However, in real-world scenarios, rotary machinery usually operates at varying speeds, making the fault-related pulse features susceptible to noise oversaturation. To extract discriminative features from mechanical signals under non-stationary conditions, a global contextual feature aggregation network (GCFAN) is developed in this paper. To begin with, a global contextual module (GCM) is embedded in the CNN architecture to explore multimodal features. Then, a multiscale attention module (MSAM) is introduced to guide the model to focus on global and local discriminative information. Further, a multiscale feature enhancement module (MFEM) is established to enlarge the receptive field and eliminate useless features. Finally, the GCFAN architecture is constructed based on these improvements. To achieve favourable diagnostic results under fluctuating variable speed conditions, we apply the label smoothing algorithm and the AMSGrad algorithm to assist the training of the model. Two case studies using the benchmark variable speed bearing dataset and the HF-MS variable speed gearbox dataset were carried out to test the practicality of the developed approach. Experimental results demonstrated that the developed GCFAN performs better than seven state-of-the-art approaches.