# Fault Diagnosis of a Solenoid Valve Based on Multi-Feature Fusion
## 1 Introduction  
In the process of the development of mechatronics, the scale and structure of mechanical-electrical-magnetic equipment have become increasingly complex. Inevitably, this complexity increase requires enhanced system component performance and increases the probability of system failures. The latter reduces the reliability, stability and safety of system's operation. The solenoid valve is an essential mechanical-electrical-magnetic component in a fluid control system and has reliability, stability and safety indicators, significantly affecting its operating performance and the overall hydraulic system efficiency. Therefore, it is crucial to further expand solenoid valve fault diagnosis technology research and other mechanical-electrical-magnetic equipment to avoid fatal failures and to prevent production loss and casualties [1, 2]. With the improvement of automation, fault diagnosis research for solenoid valves and other mechanical-electrical-magnetic equipment has been exploiting simple detection instruments and has been relying on personal experience to collect sensor-based information and to perform fault diagnosis. Based on the oil circuit switching characteristics of the solenoid valve, existing research studies the solenoid valve fault diagnosis problem on the basis of oil circuit flow and pressure [1]. For example, [PERSON] used an expert system to detect and diagnose faults in hydraulic equipment [3]. [PERSON] diagnosed faults in hydraulicsystems on the basis of dynamic flow soft measurement technology [4]. [PERSON] explored the impact of pump pressure pulsation on equipment [5]. Fault diagnosis using an expert system is suitable for cases involving many fault rules, for clear fault reasoning and for a high resolution of fault logic decision-making process. Specifically, detecting solenoid valves through interventional means, e.g., flow or pressure, is not trivial and may even damage the original hydraulic system. Non-intrusive solenoid valve diagnosis methods, vibrating the valve body, magnetic field and drive end current [1] in analyses, present advantages of easy operation and easy implementation. For example, [PERSON] detected and diagnosed equipment by detecting the sound of the machine when it was running [6], and [PERSON] analyzed the stator current for diagnosing large equipment failures [7]. [PERSON] studied the failure process of solenoid valves through current signal detection [8]. The method of state monitoring and solenoid valve fault diagnosis on the basis of current detection is easy to engineer without damaging the original hydraulic system. However, [PERSON] only analyzed the current characteristics during the failure process of the solenoid valve and did not study the diagnosis methods under each fault state. Other solutions involve solenoid valve fault diagnosis on the basis of the current detection of the drive end [1] using BP (back propagation) neural networks. In this method, the network analyzes the spring fracture and the spool stuck cases, but it does not analyze the stuck process between normal and stuck. As expected, intuitively obtaining useful information from the measured current signal is extremely hard, and thus, analysis methods such as time-domain, frequency-domain and time-frequency domain have been employed. Time-domain analysis refers to the control system analyzing a system's stability and its transient and steady-state performance under a specific input and according to the time-domain expression of the output. The latter includes filtering, amplification, calculating statistical characteristics of the signal in the time-domain and performing correlation analysis. The time-domain analysis method can effectively improve the signal-to-noise ratio, can obtain the similarity and relevance of signal waveforms at different moments, can obtain its characteristic parameters reflecting the operating state of the equipment and can provide adequate information for system dynamic analysis and fault diagnosis.