## 1 Introduction  
The operation process of the electromechanical systems of expressways is uninterrupted for 24 h, which plays an important role in the smooth operation of the national highway network, and the occurrence of all kinds of equipment failures in the electromechanical system will directly affect the actual operation efficiency of the expressway, which means the accurate calculation of the equipment failure rate. Developing reliability-centric maintenance and replacement strategies is important to ensure service reliability and security [1]. For this situation, various prediction models such as Bayesian prediction [2, 3],the support vector machine model [4; 5; 6], and the logical regression model [7; 8] have also developed rapidly and have been widely used in the field of engineering and technology. This kind of model can be used to calculate the probability of events according to some specific conditions, and the accuracy of the prediction results is more reliable. However, in the prediction of the fault types of mechanical and electrical equipment on expressways, there is still much room for improvement in the prediction and application of the above models. Limited by the operational environment of the mechanical and electrical equipment in the outfield of the highway, all kinds of environmental parameters and external stress conditions of the equipment during operation are multi-dimensional and dynamic, so it is difficult to monitor and record them in real time, which leads to the failure of the equipment. Its parameter changes are easy to miss. According to the current management strategy in most areas, maintenance operations are usually carried out only when errors are reported by the system or found manually after the failure of highway mechanical and electrical equipment. The lack of prior control has dramatically affected the reliability and remaining life of the equipment [9; 10]. On this basis, some scholars have found that using the Markov process [11; 12] to predict the failure rate can effectively avoid the problem that the early data are not applicable and the parameter records are missing when the equipment failure occurs. The prediction and analysis of equipment failure can be realized by constructing and analyzing the Markov fault model. [PERSON] [13] and other scholars established a Markov chain model by analyzing fault data and used this model to predict the occurrence frequency of different fault levels of ATCAS software in the next few months. [PERSON] [14] and others put forward a highway electromechanical equipment life prediction model based on the Markov chain, which provides a basis for expressway electromechanical equipment maintenance and renewal decision making. The new probabilistic modeling framework model for long-term fatigue reliability analysis of wind turbines proposed by [PERSON] [15] makes innovative use of the Markov chain model and sub-simulation set and is an effective tool for the long-term fatigue reliability analysis of wind turbines and other structures. [PERSON] et al. [16] constructed a simplified reliability evaluation model of a complex security system based on the Markov state. The system reliability deviation between the simplified and complete models is less than 0.0021%, proving the simplified model's effectiveness. [PERSON] [17] proposed a ten-state extended Markov model of CVT (Continuously Variable Transmission) and proved by simulation that the compensating reactors in the second capacitance group and EMUs (Electric Multiple Units) subsystem play an important role in the performance of the CVT. [PERSON] [18] and others established a semi-Markov model (UPSM) of uncertain seepage to describe the physical characteristics of traffic accidents considering random and cognitive uncertainty. In summary, using Markov theory for related prediction and analysis can significantly improve the stability of equipment operation and maintenance from the external operation and maintenance.