## 1 Introduction  
As it is easy to get a fair  
motor shaft, the gearbox bearing supports the movement of the gearbox shaft, and the axle box bearing directly supports the rotation of the wheel axle. These bearings work together to ensure the stability, smoothness, and ride comfort of the train during high-speed operation, which is an important guarantee of the train's quality. However, the service environment of rolling bearings is usually very harsh, and the bearings not only need to withstand the huge impact load from wheel-rail contact during service but also to deal with the centrifugal force brought by high-speed rotation, temperature changes, fluctuations in lubrication conditions and the intrusion of external pollutants such as dust and moisture. These factors work together to make rolling bearings prone to wear, fatigue cracks, spalling and other failures during long-term service. Once a bearing malfunctions, it can cause abnormal vibrations and noise at the very least, and at worst, it may lead to severe incidents such as equipment damage or train derailments, resulting in significant economic losses and even posing a threat to the safety of passengers and crew. Therefore, it is particularly important to carry out real-time monitoring and health assessment on the rolling bearings, which can advance the fault detection threshold, reduce the risk of unplanned downtime of equipment, and significantly extend the service life of bearings. Through advanced sensing technology, physical parameters such as vibration and temperature during bearing operation can be collected in real-time. Combined with the actual situation, the characteristic index of vibration acceleration has become the most commonly used monitoring parameter because of its high sensitivity, easy collection and processing, and the bearing health assessment based on the characteristic indicators of vibration, which is the most common. The development of fault diagnosis technology began in the 1960s [21]. The bearing fault diagnosis method based on signal processing technology was widely used. [PERSON] [21] used time-domain synchronous equalization to improve the periodic components of oscillating signals, thereby enhancing the accuracy of diagnosing faulty bearings. [PERSON] [21] applied adaptive noise reduction to the diagnosis of the slight weakness fault of the bearing. [PERSON] [21] applied the method combining variable-scale stochastic resonance and weighted kurtosis index to the diagnosis of fault signals. The basic process is to first extract the multi-source parameters of key components, such as vibration, temperature and other parameters, for real-time collection and analysis to determine whether their service status is normal. If the monitoring parameters exceed the threshold or show abnormal characteristics, the fault diagnosis mechanism is triggered. At present, the in-transit monitoring of rolling bearings is mostly completed by setting a fixed fault warning threshold, but in the actual monitoring process, there are often false positives and missed positives [21]. This is because the degradation and failure of the physical properties of the bearing will aggravate the vibration, and the amplitude of vibration acceleration will increase. However, when the external working conditions change during service, such as the speed increases, the vibration amplitude will also increase, which will affect the evaluation of the bearing performance. Therefore, in the health assessment of rolling bearings, reducing the impact of varying operating conditions and improving the accuracy of health assessment are critical and challenging focuses of current research [21]. With the development of the technology, an increasing number of neural network models are being applied to the health assessment of rolling bearings. [PERSON] [21] proposed a performance degradation evaluation method based on long short-term memory and recurrent neural networks to evaluate the real-time running state of bearings.