Fault Diagnosis of Diesel Engine Valve Clearance Based on Variational Mode Decomposition and Random Forest  
[PERSON]  
1 Beijing Key Laboratory of High-end Mechanical Equipment Health Monitoring and Self-Recovery, Beijing University of Chemical Technology, Beijing 100029, China; [EMAIL_ADDRESS] (N.Z. ); [EMAIL_ADDRESS] (J.Z.) [PERSON]  
2 Key Lab of Engine Health Monitoring-Control and Networking of Ministry of Education, Beijing University of Chemical Technology, Beijing 100029, China; [EMAIL_ADDRESS] (H.Z. ); [EMAIL_ADDRESS] (Z.J.) Dongfeng Motor Corporation Technical Center, Wuhan 430100, China; [EMAIL_ADDRESS] (Correspondence: [EMAIL_ADDRESS]  
[PERSON]  
The common methods used for the research of fault diagnosis are divided into two types. One type of research is to identify the state of the valve, to distinguish between good and faulty valves, and to indicate abnormal alarms. Another is to directly diagnose the specific fault type of valve [3]. The first type is the widest research area and is very important for timely warning when the equipment has a problem. This article includes two layers of content. Besides the first type, methods for identifying specific fault types of valve clearance on the premise of abnormal warning are also discussed here. Measurement methods used for condition monitoring and diagnosis of diesel engines mainly include vibration, acoustic emission, cylinder pressure, etc., which has shown good performance in the signal processing field. In all of these ways, vibration signal on the surface of cylinder head of diesel engine is easy to obtain, and contains abundant state information of a machine, such as in-cylinder combustion, valve opening, and valve closing [4]. However, due to the large number of excitation sources and the influence of external noise, vibration signal is usually non-stationary and non-linear [5]. Thus, the methods used in the past are not applicable, including fast Fourier transform (FFT) analysis, cepstrum analysis, the envelope spectra technique, and higher-order statistics [6]. Researchers have tried various ways to extract effective feature parameters from surface vibration signals. [PERSON] et al. [7] used the wavelet packet method to analyze diesel engine valve seating's excitation response signal segment, and diagnosed the valve clearance fault by calculating the root mean square (RMS) value of the energy. [PERSON] et al. [8] used empirical mode decomposition (EMD) and support vector machine (SVM) to extract the energy characteristics of modal components to identify the state of diesel engine. [PERSON] et al. [9] used the local mean decomposition (LMD) method to extract the feature vector of a cylinder head's vibration signal and applied SVM to identify the valve fault. However, wavelet packet decomposition needs to determine the proper wavelet basis and the number of decomposition layers. Although EMD and LMD methods can achieve the adaptive decomposition process of non-stationary signals, they all have a mode mixing phenomenon, and their robustness is poor. In order to overcome this shortcoming, the ensemble empirical mode decomposition (EEMD) method was proposed, so it had received extensive attention in the field of fault diagnosis. However, white noise was still left in the signal decomposed by the EEMD method. In order to eliminate white noise, the average number of integrations also increased, which resulted in lower data processing efficiency [10]. Recently, a new non-stationary signal processing method called the variational mode decomposition (VMD) algorithm has been proposed. VMD can determine the relevant bands of fault feature and obtain the modes concurrently by decomposing the original signal, separating the signal into several intrinsic mode functions (IMFs) [11]. Because of non-iterative decomposition and adaptively-chosen bands, VMD is able to overcome the mode mixing and misclassification problem caused by fixing band allocation [12]. At the same time, it also has better robustness and higher data processing efficiency.