Folding Convolutional Neural Network for Rotating Machinery Fault Diagnosis

One-dimensional convolutional neural network (1D-CNN)-based methods are widely used in fault diagnosis for rotating machinery. However, traditional 1D-CNN-based approaches are limited by the fixed receptive field of their convolutional kernels, which restricts their ability to capture long-range dependencies in signal data. To address the issue, a folding convolutional neural network (FCNN) is proposed. By folding the input signals before convolution, the FCNN enables a relative shift between distant information and the CNN's receptive field, allowing the model to effectively utilize both local and long-range features. Building on the FCNN, a multiview fault diagnosis framework is constructed through the integration of a transformer encoder and a long short-term memory (LSTM) network. Transfer learning is employed to optimize the initial parameters of the model. The proposed method first uses a three-layer FCNN to extract features from each channel individually, followed by feature fusion. The fused features are then analyzed by a transformer encoder, which captures global contextual relationships through self-attention, and further processed by an LSTM to model temporal dynamics. Experiments conducted on one private and three public datasets demonstrate the effectiveness, robustness, noise tolerance, and advanced performance of the proposed model.