The electromagnetic wear particle detector has been widely studied due to its prospective applications in various fields. In order to meet the requirements of the high-precision wear particle detector, a comprehensive method of improving the sensitivity and detectability of the sensor is proposed. Based on the nature of the sensor, parallel resonant exciting coils are used to increase the impedance change of the exciting circuit caused by particles, and the serial resonant topology structure and an amorphous core are applied to the inductive coil, which improves the magnetic flux change of the inductive coil and enlarges the induced electromotive force of the sensor. Moreover, the influences of the resonance frequency on the sensitivity and effective particle detection range of the sensor are studied, which forms the basis for optimizing the frequency of the magnetic field within the sensor. For further improving the detectability of micro-particles and the real-time monitoring ability of the sensor, a simple and quick extraction method for the particle signal, based on a modified lock-in amplifier and empirical mode decomposition and reverse reconstruction (EMD-RRC), is proposed, which can effectively extract the particle signal from the raw signal with low signal-to-noise ratio (SNR). The simulation and experimental results show that the proposed methods improve the sensitivity of the sensor by more than six times.

The electromagnetic wear particles detection sensor has been widely studied due to its ability to monitor the wear status of equipment in real time. To precisely estimate the change of the magnetic energy of the sensor coil caused by the wear particles, the magnetic property models of wear particles under the alternating magnetic field was established. The models consider the hysteresis effect and the eddy current effect of the wear particles. The analysis and experimental results show that with the increase of the effective field frequency, the change of the magnetic energy caused by the wear particles gradually decrease, which makes the induced electromotive force output by the sensor reduce with the decrease of the particle speed, so a signal compensation method is presented to obtain a unified signal when the same wear particle passing through the sensor in different speeds. The magnetic coupling effect between the two adjacent wear particles is analyzed. The result illustrates that the change of the magnetic energy caused by the dual wear particles system is larger than the sum of the energy variation caused by two independent wear particles, and with the increase of the interparticle distance, the magnetic coupling effect gradually weakens and disappears.

The wet clutch is a critical component of the comprehensive transmission system, and its reliability determines the stability of heavy equipment operation. As the core component of the wet clutch, the life of the wet friction components directly affects the reliability of the wet clutch. To accurately predict the wet friction components remaining useful life (RUL), an improved Wiener degradation model is established. Compared to traditional models, it sets drift parameters as random parameters to consider the randomness of degradation process. The life and degradation data of wet friction components are obtained through accelerated life test, and the composite degradation indicator is constructed by screening and fusing degradation data. Innovatively, we fuse life and degradation data to reduce early errors in RUL prediction of wet friction components. The result shows that the RUL prediction average absolute error of the improved Wiener degradation model is 0.31 h in the accelerated life range of 80 h–110 h, which verifies the accuracy of the model.

Predicting the coefficient of friction (COF) is essential for enhancing the efficiency and reliability of mechanical systems. Nevertheless, traditional mechanistic models relying on fixed values or fitted curves fail to accurately capture this complexity. To address this issue, this paper proposes a model for predicting the COF of wet friction components using an extreme gradient boosting (XGBoost) algorithm optimized by the sparrow search algorithm (SSA). This model effectively captures the nonlinear relationships among relative speed, pressure, temperature, and COF. As a result, the proposed SSA-XGBoost model exhibits excellent predictive performance with a root mean square error (RMSE) of only 0.063, and 88.3% of the COF predictions have a relative error of less than 1%, significantly outperforming other deep-learning algorithms. Additionally, to enhance the understanding of the COF prediction results for wet friction components, the SHapley Additive exPlanations (SHAP) model is used to explore the influence of relative speed, pressure, and temperature on the predicted COF values.

To address the low fault diagnosis accuracy of spindle bearings in vehicle transmission systems under imbalanced data, a fault diagnosis method based on Double-Discriminator Generative Adversarial Network (DD-GAN) and Convolutional Kolmogorov-Arnold Network (Conv-KAN) is proposed. DD-GAN consists of two discriminators, a generator, and a loss aggregation mechanism. The discriminators have distinct architectures, with one incorporating an attention mechanism to enhance local feature discrimination. The loss aggregation mechanism balances the outcomes of both discriminators, preventing the generator’s optimization from being dominated by one. Conv-KAN, based on Kolmogorov-Arnold Network (KAN), captures complex, nonlinear relationships in high-dimensional data. By adapting KAN to simulate convolution-like operations, Conv-KAN enhances the extraction of spatial features from input data, improving fault diagnosis accuracy. The method’s workflow involves converting the original bearing vibration signals into time-frequency images, augmenting the imbalanced data using DD-GAN to normal levels, and then training the Conv-KAN model on the augmented dataset for fault diagnosis. Experiments show that DD-GAN generates high-quality bearing signal samples, with an FID value 69% lower than WGAN-GP. When the imbalance ratio is 1:100, the proposed method achieves an F1 score 26.09% higher than WGAN-GP with CNN. At a ratio of 1:200, the F1 score improves by 31.44%. These experimental results comprehensively validate the superiority of the fault diagnosis method based on DD-GAN and Conv-KAN, demonstrating its effectiveness in addressing the issue of low fault diagnosis accuracy in vehicle transmission system spindle bearing under imbalanced data conditions.