Multi-modal fault diagnosis network based on MASDN for high-speed train wheelset bearings towards industrial variable speed conditions

Abstract

To address the challenge of fault diagnosis from high-speed train wheelset bearings towards complex industrial variable speed conditions, we propose a Multi-modal Adaptive Signal Diagnosis Network (MASDN). Firstly, a hybrid architecture combining multi-scale convolution with Informer Encoders is designed to capture time-varying speed features, which dynamically guide vibration feature extraction through adaptive convolutional kernel weighting. Subsequently, combining parallel multi-scale convolutional module enhanced by channel attention mechanisms for robust vibration feature extraction across varying operational conditions. Meanwhile, an adaptive structure incorporating multi-domain constraint loss functions is designed for optimal signal enhancement and noise suppression. On this basis, a speed-guided feature fusion strategy, which automatically prioritizes the most condition-relevant features, is introduced, employing depthwise separable convolution (DSConv) to achieve cross-scale integration of multi-modal features. Experiments indicate that MASDN effectively improves diagnostic accuracy and robustness in scenarios simulating industrial variable speed conditions with ablation studies further validating the effectiveness of each component in the model.