# Slipping Trend Prediction Based on Improved Informer
## 1 Introduction  
As the backbone of the transportation system, railway transportation has the advantages of large capacity and high efficiency. With the development of railway construction, locomotive traction power is also increasing, and the locomotive traction power depends on the adhesion between the wheel and rail. When the rail surface condition deteriorates, with the increase in traction torque and the acceleration of creep velocity, the driving force between the wheel and rail is greater than the maximum adhesion force that the wheel and rail can provide; excess force causes the wheelset to rotate sharply, causing the adhesion to be destroyed and further reducing the available adhesion force, making the condition worse and resulting in slipping and sliding. Once the wheelset slips, the locomotive driving force will suddenly drop, or even disappear, which will lead to a decrease in velocity. In severe cases, it will even lead to rail scratches and wheelset set tread peeling. Locomotives experience similar effects in the case of braking: excess braking force will cause the wheelset velocity to be lower than the vehicle velocity, resulting in sliding. In severe cases, the wheelset will be locked, which causes serious damage to the wheelset and the track [1]. It is of great significance to effectively predict slipping in order to enhance locomotive operation safety and reduce energy loss. During locomotive operation, the TCU produces large amounts of operation data, which provide a large number of samples for analysis byresearchers, and the multiple wheel shaft slipping states in these data constitute the timing information that is mainly analyzed in this paper. The ability to predict the slipping states efficiently and accurately is the focus of this paper. In recent years, there have been many studies on slipping detection and adhesion control. The hybrid adhesion control method is a widely used adhesion control method [2; 3; 4]. This method first calculates slip velocity, acceleration, and acceleration differential, then compares them with predefined thresholds to determine whether the locomotive has slipped based on numerical values and conditions. However, this approach is susceptible to wheel-rail noise interference, relies on manually set parameters, and introduces human-induced errors, ultimately affecting detection accuracy. With the advancement of machine learning and deep learning algorithms, some machine learning algorithms, such as neural networks, clustering, and other methods, have begun to be applied to state estimation. Reference [5] proposes a method combining fuzzy neural networks and unscented Kalman filtering for train anti-slip control. UFK is suitable for the time domain filtering of recursive Gaussian linear systems, and performs well in the state estimation of dynamic linear systems subject to Gaussian distribution. However, in complex and changeable environments, UFK performs poorly and has poor robustness. Moreover, fuzzy neural networks have high computational complexity, difficult rule optimization, low interpretability, and limited generalization ability. Reference [6] proposes a locomotive slipping detection method based on empirical wavelet transform (EWT), fuzzy entropy (FE), and SVM. The algorithm decomposes the speed data by EWT, extracts the feature information related to slipping by the fuzzy entropy algorithm, and then trains and optimizes SVM by particle swarm optimization algorithm. The method has a high accuracy and recall rate, but the calculation process is complex and the detection rate is low. It cannot predict the locomotive data online in real time. Reference [7] proposes a method that combines wavelet analysis with an LSTM+MLP model to extract short-term and long-term features of the slip state for slip detection and recognition. However, the computational load of this model is relatively high, which may result in slower response times and delays in feedback on the detected slip state.