## 1 Introduction
[37] developed a new numerical simulation method to monitor the RC valves status while using the instantaneous angular speed (IAS) of crankshaft and cylinder pressure. A modular fault diagnosis and prediction method of hydraulic valve system based on multi-sensor data redundancy was proposed in [10]. [PERSON] et al. [23] used big data and machine learning methods to identify and classify RC faults based on multi-signal such as vibration, temperature, and displacement. [PERSON] et al. proposed an approach to fault diagnosis of RC valves while using Teager-Kaiser energy operator and deep belief networks based on vibration, pressure, and current signals [38]. Although these studies make up for the singleness of signal, the lack of correlation between multiple signals makes it necessary to consider the fusion of different signals, which increases the difficulty of the signal processing methods. This paper proposes a quantitative diagnosis method of RC valve leakage based on system characteristics. The current signal and vibration signal are used as the excitation signal and the response signal, respectively. The excitation and response signals have a natural system transfer relationship, which can maximize the potential of the signal. Therefore, the problem of a small amount of information in a single signal is solved, and the problem of multi-signal coupling is avoided. Additionally, the mathematical model of the RC motor current was established, which constitutes a fault diagnosis method that combines model and data-drive, and it can reduce the performance requirement of signal processing method. In addition, the diagnosis object can be regarded as a \"blackbox\", without the need for detailed parameters inside the diagnosis object, which greatly reduces the impact of modeling difficulties on the diagnosis effect [39]. The ensemble empirical mode decomposition (EEMD) algorithm maintains the adaptive decomposition of the empirical mode decomposition (EMD) and overcomes the endpoint effect and mode aliasing effect of EMD [40; 41]. However, the selection of the intrinsic mode function (IMF) is still a problem to be solved [42; 43]. The optimal IMFs are selected to reconstruct the excitation signal, and the kurtosis, RMS, and correlation coefficient are calculated as the excitation condition indicators (CIs), according to the side frequency effect of the current signal and the rotational frequency effect of the RC. The vibration signal of RC is periodic, and accompanied by the frequency doubling component. The comb filter is composed of a number of passbands and stopbands arranged at a certain frequency interval, only allowing signals of certain frequency range to pass, and it is a good tool for extracting faulty characteristic information [44; 45; 46]. By using comb filter and setting appropriate basic frequency and bandwidth, the energy spectrum values of RC rotational frequency component and its two-fold and three-fold frequency components contained in vibration signal were extracted as the CIs of the response. SVM has unique advantages for solving small samples, nonlinearity, and high dimension classification problems [47; 48], and it has good robustness and generalization ability [49; 50]. Based on the CIs of excitation, response, and system, the SVM was used to conduct the contrast experiment of different fault pattern. At the same time, on the basis of classification, a \"three - regions, four-point sets\" fault diagnoser was constructed to quantitatively diagnose for different severity of RC valve leakage. The rest of this paper is organized, as follows. Section 2 presents a framework of the proposed method, established the RC motor current model, and analyzed the influence mechanism of valve leakage on RC vibration. Section 3 introduces the experimental test-rig and conditions of data collection. The failure characteristics of excitation signal and response signal are analyzed, and the CIs are extracted in Section 4.