# Fault-Diagnosis Method for Rotating Machinery Based on SVMD Entropy and Machine Learning
###### Abstract  
Rolling bearings and gears are important components of rotating machinery. Their operating condition affects the operation of the equipment. Fault in the accessory directly leads to equipment downtime or a series of adverse reactions in the system, which brings enormous pecuniary loss to the institution. Hence, it is of great significance to detect the operating status of rolling bearings and gears for fault diagnosis. At present, the vibration method is considered to be the most common method for fault diagnosis, a method that analyzes the equipment by collecting vibration signals. However, rotating-machinery fault diagnosis is challenging due to the need to select effective fault feature vectors, use appropriate machine-learning classification methods, and achieve accurate fault diagnosis. To solve this problem, this paper illustrates a new fault-diagnosis method combining successive variational-mode decomposition (SVMD) entropy values and machine learning. First, the simulation signal and the real fault signal are used to analyze and compare the variational-mode decomposition (VMD) and SVMD methods. The comparison results prove that SVMD can be a useful method for fault diagnosis. Then, these two methods are utilized to extract the energy entropy and fuzzy entropy of the gearbox dataset of Southeast University (SEU), respectively. The feature vector and multiple machine-learning classification models are constructed for failure-mode identification. The experimental-analysis results successfully verify the effectiveness of the combined SVMD entropy and machine-learning approach for rotating-machinery fault diagnosis. rotating machinery; fault diagnosis; successive variational-mode decomposition; machine learning; entropy +
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In the process of fault diagnosis, the collected vibration signals are used to estimate the occurrence of the fault. However, the collected fault-vibration signals are characterized by nonlinear and non-smooth status. An algorithm is required to decompose the fault-vibration signals into multiple smooth signals and extract the feature signals. Therefore, a series of signal-decomposition algorithms was developed [7] that greatly promoted the development of theoretical research and engineering applications in the field of fault diagnosis. The requirement of wavelet-transform application is restricted and requires expert a priori knowledge. The mother wavelet and decomposition levels in particular need to be manually determined in advance [8]. Local mean decomposition (LMD) is highly affected by noise and adds redundant frequency components when performing signal decomposition [9]. The mechanical-fault-diagnosis method based on empirical-mode decomposition (EMD) [10; 11; 12] can adaptively extract the mode components of the local eigen structure of the signal. Ensemble empirical-mode decomposition (EEMD) is a noise-assisted signal-analysis method that is added to EMD to improve the mode-aliasing phenomenon in signal decomposition [13; 14]. Complete ensemble empirical-mode decomposition with adaptive noise (CEEMDAN) is an improved EEMD method and has better performance in signal denoising [15; 16]. Intrinsic time-scale decomposition (ITD) can suppress endpoint effects more effectively and has good computational efficiency compared with EMD and LMD; however, ITD also has problems with distorting some signals during decomposing. In addition, the quality requirements of the signal are high [17; 18]. Variational-mode decomposition (VMD) is a signal-processing method proposed by [PERSON] [19] that has strong robustness to noise and excellent performance [20; 21; 22]. In recent years, many improved algorithms for VMD have been studied, but the problem of under-decomposition inevitably is caused by the limitations of the algorithm [23; 24; 25]. [PERSON] presented real case studies of fault diagnosis based on deep convolutional networks and principal component analysis [26; 27].