Aiming at the problems of modal aliasing and poor noise resistance when processing the vibration acceleration signal of rolling bearings by empirical modal decomposition (EMD), a variational modal decomposition (VMD) method based on parameter optimization is proposed. Combined with the improved particle swarm optimization algorithm (IPSO) and improved envelope entropy, the VMD decomposition layers and penalty parameters were optimized. The components with high correlation coefficients with the original signal were screened out, and the fault characteristics were extracted by combining the sample entropy. Aiming at the low classification accuracy of the support vector machine with fixed parameters in the fault diagnosis stage and the defects of the gray wolf algorithm, such as insufficient population diversity and large influence of the initial population on the optimization effect, an improved gray wolf algorithm (IGWO) based on multistrategy improvement is proposed. The IGWO was combined with the support vector machine to obtain an improved gray wolf algorithm optimization support vector machine (IGWO-SVM). The rolling bearing fault diagnosis test bench is established to collect the vibration acceleration signals of rolling bearing under different states. The experimental results show that the fault diagnosis of rolling bearings with strong noise can be effectively realized by applying the above methods, and the average fault diagnosis accuracy rate reaches 98.875%.

As one of the important parameters of bearing operation, temperature is a key metric to diagnose the state of service of a bearing. However, there are still some shortcomings in the study of the temperature variation law for damaged bearings. In this paper, according to the structural characteristics of bearings, the influence law of thermal-solid coupling effect on bearing structure is considered, and a novel transient temperature analysis model of damaged bearings is established. First, a quasi-static analysis of the bearing is performed to obtain the variation laws of the key parameters of the bearing under thermal expansion. Then, the load variation law of the bearing under the condition of damage is discussed, and the heat generation and heat transfer of the damaged bearing during operation are studied. Based on the thermal grid method, a transient temperature analysis model of the damaged bearing is developed. Finally, the model is tested experimentally and the influence of the rotate speed and load on the bearing temperature variation is analyzed. The results show that the established model can effectively predict the temperature variation and thermal equilibrium temperature of damaged bearings.

EMU (electric multiple unit) traction motors are powered by converters whose output voltage increases the voltage stress borne by the insulation system, making the ITSC (inter-turn short-circuit) fault more prominent. An index based on short-circuit thermal power is proposed in the article to evaluate the non-metallic ITSC faults extent. The apFFT (all-phase FFT) time-shift phase difference correction with double Hanning windows is used to calculate fault features to train the SVM (support vector machine) fault diagnosis model whose hyper-parameters C and g are optimized using grid search methods. The experimental verification was carried out on the EMU electric traction simulation experimental platform. According to the fault extent index proposed in this article, the experimental samples were divided into three categories, normal, incipient and serious fault samples. The ITSC fault diagnosis accuracy was 100% on the training dataset and 93.33% on the test dataset. There was no misclassification between normal and serious ITSC fault samples.

The EMU(electric multiple units) traction motors are powered by converters. The PWM(pulse width modulation) voltage increases the voltage stress borne by the motor insulation system, making the ITSC(inter-turn short-circuit) fault more prominent. An index based on short-circuit thermal power was proposed in the article to evaluate the non-metallic ITSC faults degree. The apFFT(all phase FFT) time-shift phase difference correction with double Hanning-windows is used to calculate the fundamental frequency of the traction motor's ZSVC（zero se-quence voltage component）, the fundamental amplitudes of ZSVC and three-phase current. The five parameters are used as fault features to train the SVM (support vector machine)fault diagnosis model. The SVM hyper-parameters C and g are optimized by K-CV (K fold cross-validation) and grid search methods. The experimental verification was carried out by the EMU electric traction simulation experimental platform. According to the non-metallic degree index proposed in this article, the experimental samples were divided into three categories, normal, incipient and serious fault samples. The ITSC fault diagnosis accuracy was 100% on the training data set and 93.33 % on the test data set. There was no misclassification between normal and serious ITSC fault samples.

This paper introduces an Improved Red-Billed Blue Magpie Optimizer (IRBMO) to enhance the Maximum Second-Order Cyclostationary Blind Deconvolution (CYCBD) method, which traditionally depends on manual, experience-based setting of its key parameters (filter length $L$ and cyclic frequency $\mathsf{\alpha }$). By adopting an Improved Envelope Spectrum Entropy (EK) as the fitness function, the IRBMO autonomously optimizes these parameters, eliminating the need for prior knowledge and improving its applicability in industrial settings. The Improved Red-Billed Blue Magpie algorithm is employed to adaptively optimize the penalty parameter and kernel function parameter of the support vector machine, thereby obtaining an optimal support vector machine model. By introducing fuzzy entropy theory, the feature vectors of filtered signals—processed by the Cyclostationary Blind Deconvolution method with optimal parameters—are extracted and used as input for the optimally parameterized support vector machine, achieving multi-fault classification for bogie bearings. The results show that the IRBMO-CYCBD method significantly enhances the periodic weak fault impulse components and improves the signal-to-noise ratio of the processed signal. Envelope spectrum analysis of the filtered signal allows for clear observation of shaft frequency components, as evidenced by the accurate identification of the 110 Hz fundamental frequency and its harmonic components at 220 Hz, 330 Hz, and 440 Hz in the spectrum. Simulation tests verify the efficacy of the IRBMO-CYCBD method in processing rolling bearing vibration signals under strong noise interference. Under laboratory conditions, simulation experiments were conducted by collecting vibration acceleration signals from rolling bearings in various states. The aforementioned method was applied for fault diagnosis, achieving a maximum diagnostic accuracy of 100%. Through repeated experiments, it was verified that this method meets the fault diagnosis requirements for rolling bearings in metro train bogies.

Anomaly detection of catenary support components (CSCs) is an important component in railway condition monitoring systems. However, because the abnormal features of CSCs loosening are not obvious, and the current CNN models and visual Transformer models have problems such as limited remote modeling capabilities and secondary computational complexity, it is difficult for existing deep learning anomaly detection methods to effectively exert their performance. The state space model (SSM) represented by Mamba is not only good at long-range modeling, but also maintains linear computational complexity. In this paper, using the state space model (SSM), we proposed a new visual state space reconstruction network (VSM-UNet) for the detection of CSC loosening anomalies. First, based on the structure of UNet, a visual state space block (VSS block) is introduced to capture extensive contextual information and multi-scale features, and an asymmetric encoder–decoder structure is constructed through patch merging operations and patch expanding operations. Secondly, the CBAM attention mechanism is introduced between the encoder–decoder structure to enhance the model’s ability to focus on key abnormal features. Finally, a stable abnormality score calculation module is designed using MLP to evaluate the degree of abnormality of components. The experiment shows that the VSM-UNet model, learning strategy and anomaly score calculation method proposed in this article are effective and reasonable, and have certain advantages. Specifically, the proposed method framework can achieve an AUROC of 0.986 and an FPS of 26.56 in the anomaly detection task of looseness on positioning clamp nuts, U-shaped hoop nuts, and cotton pins. Therefore, the method proposed in this article can be effectively applied to the detection of CSCs abnormalities.