## 1 Introduction  
Nowadays, micro electro mechanical systems (MEMS) devices are used in various fields, such as automotive, biomedical, aerospace, and communication technologies [1]. They play an indispensable role in functioning and protection of the entire system [2]. As one of the components, micro-switches are affected by different working cycles and unavoidable external factors, such as changes in temperature and humidity, resulting in different degrees of residual life [3]. However, micro-switches reliability has attracted little attention, whose failure may cause significant downtime, as well as safety implications. Specifically, they are important parts of rail vehicle systems, and whether they are damaged is related to the operation of the entire system or even the safety of the passengers. Due to the significance of such aspect, several research works dealing with the reliability of micro-switches and other electronic components have been published, such as references [4; 5; 6; 7; 8; 9]. Nevertheless, traditional approaches to estimate remaining useful life (RUL) have failed because of the reliance on average accumulated historical field data [10]. Reliability is estimated without taking into account the specific utilization of each component, such as working environment and using frequency. However, in practice, the lifetime should be different from one to another depending on how and where it is used. As a result, test duration and cost have become a huge challenge for traditional approaches. Real-time monitoring of the RUL of micro-switches and provide a convenient for timely maintenance decisions, is one of the important ways to improve its reliability [11; 12]. The topic of the real-time prediction of RUL for electronic devices is one of the most active areas in prognostics and health management (PHM) research today. Considered with the stochastic characteristics of RUL in stochastic dynamic processes under actual working conditions, the data-driven RUL prediction was studied in the early 1980s, [PERSON] et al. [13] confirmed the importance of life distribution in extending the life of equipment. This type of method is the most typical traditional life prediction method. The statistical analysis of life data determines the probability distribution of equipment life. However, the equipment such as micro-switches, owned high reliability and long-life features and it failed to obtain sufficient time to failure data in the short term, which made it difficult to obtain satisfactory prediction results for traditional life analysis methods based on life data. In recent years, there has been an increasing interest in the establishment of the real-time life prediction model by using monitoring data and calculating the probability distribution in the use of statistical methods. A large amount of literature has been delivered, [PERSON] et al. [14] summarized the commonly used assumptions applying a random coefficient regression model and proposed a method for determining the failure threshold by optimization. Furthermore, [PERSON] et al. [15] proposed a logarithmic linearized exponential-like random coefficient regression model. The model utilized historical degradation data from similar devices and incorporated real-time monitoring data from historical data of service equipment through the Bayesian updating mechanism to update the remaining life distribution. [PERSON] et al. [16] summarized the experience of predecessors and provided an effective theory and method for establishing stochastic degradation models and studying RUL prediction problems. Especially, the algorithm put forward by [PERSON] et al. [17] is widely used but there still exists some unsolved problems, such as, it is not sensitive to the initial real-time monitoring degradation data due to the objects of micro-switches. In this paper, a linear degradation model based on an inverse Kalman filter to imitate the stochastic deterioration process was proposed.