Feature extraction method based on VMD and MFDFA for fault diagnosis of reciprocating compressor valve

Abstract

Aiming at the nonlinearity, nonstationarity and multi-component coupling characteristics of reciprocating compressor vibration signals, an integrated feature extraction method based on the variational mode decomposition (VMD) and multi-fractal detrended fluctuation analysis (MFDFA) is proposed for a fault diagnosis for a reciprocating compressor valve. Firstly, to eliminate the noise interference, a novel VMD method with superior anti-interference performance was utilized to obtain several components of the quasi-orthogonal band-limited intrinsic mode function (BLIMF) from a strong non-stationarity vibration signal, and a consistent number K of BLIMFs was selected based on a novel criterion for all fault states. Secondly, the MFDFA method, which can describe the multi-fractal structure feature of non-stationary time series, was applied to analyze each BLIMF component, and the parameters of MFDFA were employed as the eigenvectors to reflect the structure characteristics and local scale behavior of the vibration signal. Then, the principal component analysis (PCA) was introduced to refine the eigenvectors for a higher recognition efficiency and accuracy. Finally, the vibration signals of four types of reciprocating compressor valve faults were analyzed by this method, and the faults were identified correctly by pattern classifiers of BTSVM and CNN. Further results comparison with other feature extraction methods verifies the superiority of the proposed method.

Fault Diagnosis Method Based on Modified Multiscale Entropy and Global Distance Evaluation for Valve Fault of Reciprocating Compressor

“…The variational mode decomposition (VMD) method [19] and [20] is an adaptive signal processing method, successfully utilized for extracting the fault features for nonlinear and non-stationary rub-impact signals [21] to [23], the instantaneous detection of speech signals [24] and trends analysis of financial markets [25]. Therefore, a feature extraction algorithm in combination with VMD and MMSE can improve the accuracy of feature extraction.…”

A fault diagnosis method on GWO-SPA and MAE for fault diagnosis of reciprocating compressors

“…As a result, the unobvious physical meaning of each component can affect the accuracy of fault diagnosis. Reference [6] used the variational mode decomposition (VMD) to decompose the signal and multifractal detrended fluctuation analysis (MFDFA) extracted reciprocating compressor valve fault features. Although the MEA-2022 Journal of Physics: Conference Series 2528 (2023) 012037 IOP Publishing doi:10.1088/1742-6596/2528/1/012037 2 nonlinear signal analysis has been effectively applied, these methods are affected by multiple parameters and the number of components to be decomposed.…”

Health condition monitoring of bearings based on multifractal spectrum feature with modified empirical mode decomposition-multifractal detrended fluctuation analysis

“…In view of the conspicuous difference in multifractality characteristics of vibration signals under different bearing health conditions due to different dynamic mechanisms, the extraction method based on multifractal features is applied to fault diagnosis and detection in practical application. 20,21 Furthermore, some studies focused on the application of adaptive signal decomposition methods to MFDFA, such as Local Characteristic-scale Decomposition (LCD), 22 Intrinsic Time-scale Decomposition (ITD), 23 EMD, 18 and Variational Mode Decomposition (VMD), 24 which are employed to decompose a signal into a series of sub-signals with different local scales, and improve the accuracy of fault diagnosis by extracting the inherent fractal features of this sub-signals. Although the effective behavior of multifractal characteristics in recognition of rotating machinery including diverse fault types and severity under different running conditions was verified by experimental results in the above researches, they cannot still fully explain the effectiveness of multifractal features for monitoring the health condition of the complex system in real time.…”

“…In view of the conspicuous difference in multifractality characteristics of vibration signals under different bearing health conditions due to different dynamic mechanisms, the extraction method based on multifractal features is applied to fault diagnosis and detection in practical application. 20,21 Furthermore, some studies focused on the application of adaptive signal decomposition methods to MFDFA, such as Local Characteristic-scale Decomposition (LCD), 22 Intrinsic Time-scale Decomposition (ITD), 23 EMD, 18 and Variational Mode Decomposition (VMD), 24 which are employed to decompose a signal into a series of sub-signals with different local scales, and improve the accuracy of fault diagnosis by extracting the inherent fractal features of this sub-signals.…”