# Failure Type Prediction Using Physical Indices and Data Features for Solenoid Valve +
## 1 Introduction  
The braking system is a part of high-speed train safety systems [1]. The high-speed solenoid valve is a critical component in the braking system [2]. Accurately predicting the failure type of the solenoid valve is an important guarantee for safe operation of the braking system. In general, methods of prognostic and diagnostics can be categorized into physics-based, data-driven, and hybrid approaches [3, 4]. The physics-based method uses a specific physical model to represent the normal machine state and detects the potential failure types based on the deviations between the actual system and the physical model [5, 6, 7, 8, 9, 10, 11]. [PERSON] et al. [5, 6] used frequency response analysis diagnostics to improve the fault detection of power transformer winding. [PERSON] et al. [7] presented an electromechanical model of a rotating machine for its diagnosis. [PERSON] et al. [8] proposed a physics-based prognostic model for the rolling element bearings usingthe realized volatility and wavelet neural network to predict their remaining life. [PERSON] et al. [9] integrated a physics-based clogging progression model with particle filter to predict the future clogging levels and the remaining useful life of the fuel filters. However, due to the electrical, magnetic, and mechanical coupling aging mechanism; individual differences; and uncertainty of aging process [12], it is difficult to establish an accurate and reliable physical model for the solenoid valve. Data-driven approaches use information from previously collected data to identify the relationship between the failure and the measurement of system state and do not depend on the physical mechanism or prior knowledge of the system [13; 14; 15; 16; 17; 18]. [PERSON] et al. [13] proposed an improved fault prognostic approach based on a modified particle filter with a built-in differential evolution characteristic. [PERSON] et al. [14] developed a two-stage data-driven prognostic approach based on an enhanced Kalman filter and an expectation-maximization algorithm to estimate the remaining useful life of the bearing. [PERSON] et al. [15] aggregated predictions of long short-term memory and convolutional long short-term memory via a meta model to boost the performance of diagnostic models for automotive. [PERSON] et al. [16] presented a unified fault diagnosis and prognosis strategy to identify faults and to predict the remaining useful life for solid oxide fuel cell systems. The disadvantage of data-driven approaches is that they rely heavily on the failure histories of the system components collected from the field or laboratory. Hybrid approaches combine the physical model and date-driven method to improve the prediction performance [19; 20; 21]. However, existing researches of hybrid approaches focus on the fault diagnosis and the remaining useful life prediction of devices, few of which pay attention to the prediction of the type of failure. In our previous research, we proposed an adaptive data-driven approach which combines Bayesian updating and particle filter to predict the remaining useful life for solenoid valve [22]. Based on the prediction result, the solenoid valve could be timely replaced before it fails. Furthermore, in the paper, the failure type can be determined and the proper maintenance decision can be obtained. For instance, the high-speed solenoid valve with aged coil can only be replaced to ensure the reliability of the braking system, while it is more reasonable to repair the solenoid valve by lubricating with oil rather than replacing it directly. However, in actual operation, the aging of the high-speed solenoid valve is usually the result of a combination of both factors. Thus, we develop a novel method combining the physical modeling and history failure data to accurately predict the failure type of the solenoid valve in this paper. In this paper, a failure type prediction method for high-speed solenoid valve is proposed to address the aforementioned problems and uses physical indices and data features.