Online wear particle detectors have been widely studied due to their ability to monitor the wear state of machines, and to help to prevent serious mechanical failures caused by excessive wear of components. This paper presents a review of the state-of-the-art wear debris detectors based on different principles. That mainly includes optical/imaging particle detectors, electrical particle detectors, ultrasonic particle detectors and magnetic debris detection sensors. Meanwhile, the characteristics and the performance (sensitivity, maximum flow rate, and the detectable information) of each type of sensor are detailed discussed. In conclusion, the optical/imaging and magnetic debris detectors have become promising technologies in the field of wear monitoring. Meanwhile, that the rapid extraction algorithm and three-dimensional reconstruction method of wear debris for optical/imaging debris detectors, and improving the sensitivity and detectability of magnetic debris detectors are the two important research contents in the field. Besides that, it can be predicted that the integrated wear monitoring system that can collect multiple wear-information in-situ, and the intelligent wear particle detector that have the ability of wear monitoring, wear evaluation and fault warning are the main research directions in the future.

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.

A parallel three-coil wear particle sensor is innovatively proposed with the detachable substrate and magnetic rings. The magnetic rings are designed and fabricated using polydimethylsiloxane (PDMS) and iron-based amorphous soft magnetic material (IASM). The excitation coils surround the magnetic rings, leaving only a 1mm air gap on the inner side (near the flow channel). The magnetic rings are magnetized under the action of the high-frequency alternating magnetic field, which forms an enhanced magnetic field with a high gradient distribution in the detection area. Through numerical simulation and experimental verification, the influence of IASM concentration, radial position and size of wear particles on the sensor output signal are studied. With the increase of IASM concentration, the sensor detection sensitivity rises dramatically. The sensor with 75% IASM magnetic rings can effectively detect 60 $\mu \text{m}$ iron particles and 160 $\mu \text{m}$ copper particles in a 10mm flow channel; the detection error rate is less than 15% in the actual lubrication circuit, which fully meets the needs of early abnormal wear fault diagnosis of mechanical equipment. Therefore, this method is a promising candidate for developing the large-flow electromagnetic wear particle sensor sensitivity.

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.