Dual-Channel Rolling Bearing Fault Diagnosis Method Based on ACNN-LFSwin Transformer
摘要
Rolling bearings are components commonly used in mechanical equipment. Traditional methods struggle to classify signals with numerous complex features in a multi-noise environment. They often rely on classical deep learning models for performing fault diagnosis using one-dimensional data, failing to fully extract complex features. To address this issue, this paper proposes a dual-channel fault diagnosis method based on the ACNN-LFSwin Transformer, which performs fault diagnosis on both one-dimensional data and two-dimensional images. First, the original signal is processed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Short-Time Fourier Transform (STFT) to obtain Intrinsic Mode Functions (IMF) and two-dimensional images. Subsequently, in channel 1, the CEEMDAN-decomposed IMF are fed into an Attention-based Convolutional Neural Network (ACNN) for feature extraction. In channel 2, the two-dimensional images composed of bearing data are input into a Swin Transformer network (LFSwin Transformer) for local feature extraction. Finally, the features from both channels are concatenated and fused for fault diagnosis. ACNN employs an attention mechanism to automatically allocate weights to signal features, thereby emphasizing key features. The LFSwin Transformer performs vector conversion based on the traditional Swin Transformer, converts the input vector into an image, and performs convolution operations, making the model more advantageous in extracting local fault features. In experiments on the CWRU and Paderborn datasets, the proposed method achieves a fault diagnosis accuracy of over 97%. This result shows that it can accurately diagnose various faults and effectively avoid interference from complex noise.