# CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis
## 1 Introduction  
As an essential component in life, valves mainly play a significant role in regulating flow direction, controlling the opening and closing of pipelines, and ensuring effective sealing performance, which directly impacts their functionality. During valve operation, internal leakage poses a greater challenge than external leakage due to its concealed nature, making accurate diagnosis of internal leakage particularly crucial. Currently, methods for detecting internal leakage in valves can be categorized into two main types: offline detection methods, which require disassembly of the valve, and online detection methods, which do not necessitate disassembly. Online methods include acoustic emission detection [1, 2], negative-pressure wave detection [3], and ultrasonic detection [4]. For instance, [PERSON] [5] employed ultrasonic waves to assess the leakage in check valves; however, ultrasonic signals are susceptible to interference from external noise and require active excitationsignals to generate detectable responses, resulting in limited real-time performance. [PERSON] et al. [6; 7] utilized negative-pressure waves for pipeline leakage localization; their detection capability diminishes at low leakage rates, leading to reduced accuracy. In contrast, the acoustic emission detection method offers greater convenience, adaptability, real-time responsiveness, and sensitivity. In recent years, numerous scholars have engaged in comprehensive research regarding the extraction of acoustic emission signal features associated with valve leakage. This body of work encompasses extensive investigations across the time domain, frequency domain, and time-frequency domain. In the time domain, [PERSON] et al. [8] introduced an acoustic emission signal analysis method predicated on standard deviation to identify internal valve leakage. They established a mathematical model by fitting the relationship between the standard deviation and leakage rate, utilizing the least squares method. Regarding time-frequency analysis, [PERSON] [9] employed wavelet packet transformation to decompose the signal into various frequency ranges, subsequently calculating the root mean square (RMS) value and assessing valve issues based on fluctuations in the RMS value. Additionally, [PERSON] et al. [10] utilized wavelet scattering transform (WST) to extract the first three wavelet scattering coefficients from leakage signals, which were then employed as feature vectors. The leakage acoustic emission signal from the valve exhibits nonlinear and nonstationary characteristics, which may diminish the efficacy of the previously mentioned statistical features in differentiating fault states. Adaptive time-frequency analysis methods, such as Ensemble Empirical Mode Decomposition (EEMD) [11], and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) [12], have been increasingly employed by researchers to address certain limitations of earlier signal processing techniques. In light of the fact that the emission signal from the valve can be easily influenced by environmental noise, we propose using CEEMDAN to decompose the signal. This approach aims to extract useful signals while minimizing the impact of noise. By selecting the top five IMFs based on their correlation coefficients, we can focus on the most relevant frequency bands associated with valve internal leakage. To further enhance feature extraction, fuzzy entropy is employed to quantify the complexity and irregularity of these selected IMFs, providing robust features for classification. In the realm of fault classification, the support vector machine (SVM) [13; 14; 15] is extensively utilized as an effective classification tool. SVM demonstrates a high level of accuracy in fault identification and classification, even when faced with limited data samples owing to its robust learning capabilities. In light of its strong adaptability, low classification error, straightforward feature vector, and ability to work with small datasets, SVM was chosen as the classifier.