Aiming at the problem that the vibration signals of rolling bearings in high-speed rail traction motors are often affected by noise when they are in a fault state, which makes it very difficult to extract the fault features during fault diagnosis and causes obstruction in fault classification. The article proposes a rolling bearing fault diagnosis based on optimized variational mode decomposition (VMD) combined with signal features and an improved convolutional neural network (CNN). The golden jackal optimization (GJO) algorithm is employed to optimize the key parameters of the VMD, enabling effective signal decomposition. The decomposed signals are then filtered and reconstructed using criteria based on kurtosis and interrelationship measures. The time-domain features of the reconstructed signals are computed, and the feature vectors are constructed, which are used as inputs to the deep learning network; the CNN combined with the support vector machine (SVM) network model is used for the extraction of the features and the classification of the faults. The experimental results show that the method can effectively extract fault features in noise-covered signals, and the accuracy is also significantly improved compared with traditional methods.

For mechanical equipment to operate normally, rolling bearings—which are crucial parts of rotating machinery—need to have their faults diagnosed. This work introduces a bearing defect diagnosis technique that incorporates three-channel feature fusion and is based on enhanced Residual Networks and Bidirectional long- and short-term memory networks (ResNet-BiLSTM) model. The technique can effectively establish spatial-temporal relationships and better capture complex features in data by combining the powerful spatial feature extraction capability of ResNet and the bidirectional temporal modeling capability of BiLSTM. Specifically, the one-dimensional vibration signals are first transformed into two-dimensional images using the Continuous Wavelet Transform (CWT) and Markov Transition Field (MTF). The upgraded ResNet-BiLSTM network is then used to extract and combine the original one-dimensional vibration signal along with features from the two types of two-dimensional images. Finally, experimental validation is performed on two bearing datasets. The results show that compared with other state-of-the-art models, the computing cost is greatly reduced, with params and flops at 15.4 MB and 715.24 MB, respectively, and the running time of a single batch becomes 5.19 s. The fault diagnosis accuracy reaches 99.53% and 99.28% for the two datasets, respectively, successfully realizing the classification task.