Improved uniform phase empirical mode decomposition and its application in machinery fault diagnosis

Zheng, Jinde; Su, Miaoxian; Ying, Wanming; Tong, Jinyu; Zi-wei, Pan

doi:10.1016/j.measurement.2021.109425

Cited by 80 publications

(45 citation statements)

References 20 publications

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“…Signal decomposition can extract the useful component containing fault characteristic information and thus achieve the purpose of removing noise and interference components. Among the aforementioned signal decomposition methods, VMD decomposes a signal into an ensemble of band-limited sub-signals called modes [ 19 ], and it currently receives extensive study and application due to its complete mathematical principles and ability to avoid the shortcomings of sensitivity to noise, end effects, and mode mixing, which are inherent in EMD and LMD [ 20 , 21 , 22 , 23 ]. Nevertheless, there are two critical parameters affecting the performance of VMD that need to be pre-set properly for VMD implementation: the balancing parameter and number of modes.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index

Guo

Yang

Jiang

et al. 2022

Sensors

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Rolling bearing is an important part guaranteeing the normal operation of rotating machinery, which is also prone to various damages due to severe running conditions. However, it is usually difficult to extract the weak fault characteristic information from rolling bearing vibration signals and to realize a rolling bearing fault diagnosis. Hence, this paper offers a rolling bearing fault diagnosis method based on successive variational mode decomposition (SVMD) and the energy concentration and position accuracy (EP) index. Since SVMD decomposes a vibration signal of a rolling bearing into a number of modes, it is difficult to select the target mode with the ideal fault characteristic information. Comprehensively considering the energy concentration degree and frequency position accuracy of the fault characteristic component, the EP index is proposed to indicate the target mode. As the balancing parameter is crucial to the performance of SVMD and must be set properly, the line search method guided by the EP index is introduced to determine an optimal value for the balancing parameter of SVMD. The simulation and experiment results demonstrate that the proposed SVMD method is effective for rolling bearing fault diagnosis and superior to the variational mode decomposition (VMD) method.

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Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index

Guo

Yang

Jiang

et al. 2022

Sensors

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“…Essentially, the process of fault feature extraction comprises the elimination of noise and interference components in vibration signals. An effective approach to solve this problem is signal decomposition, the variants of which include wavelet decomposition (WT) [12][13][14], empirical mode decomposition (EMD) [11,15,16], local mean decomposition (LMD) [9,10,17,18], and empirical wavelet transform [19][20][21][22]. However, WT is not a selfadaptive signal analysis method because it is restricted by the selection of the wavelet basis function and number of decomposition levels [17].…”

Section: Introductionmentioning

confidence: 99%

“…However, WT is not a selfadaptive signal analysis method because it is restricted by the selection of the wavelet basis function and number of decomposition levels [17]. Although EMD can self-adaptively decompose a multimodulated signal into a series of intrinsic mode functions (IMFs), it lacks a theoretical basis and has some inherent defects, such as sensitivity to noise, end effects, and mode mixing [4,5,15,16]. Like EMD, LMD adaptively decomposes a multicomponent signal into several single-component AM-FM signals but encounters several technical problems, such as end effects and mode mixing [17,18].…”

Section: Introductionmentioning

confidence: 99%

Gearbox Fault Diagnosis Based on Improved Variational Mode Extraction

Guo

Jiang

Yang

et al. 2022

Sensors

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Gearboxes are widely used in drive systems of rotating machinery. The health status of gearboxes considerably influences the normal and reliable operation of rotating machinery. When a gearbox experiences tooth failure, a vibration signal with impulse features is excited. However, these impulse features tend to be relatively weak and difficult to extract. To solve this problem, a novel approach for gearbox fault feature extraction and fault diagnosis based on improved variational mode extraction (VME) is proposed. Since the initial value of the desired mode center frequency and the value of the penalty parameter in VME must be assigned, a short-time Fourier transform (STFT) was performed, and a new index, the standard deviation of differential values of envelope maxima positions (SDE), is proposed. The feasibility and effectiveness of the proposed approach was verified by a simulation signal and two datasets associated with a gearbox test bench. The results demonstrate that the VME-based approach outperforms the variational mode decomposition (VMD) approach.

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“…In the process of mapping the signal to be decomposed to the corresponding frequency band of the white noise, modal aliasing appeared easily [ 13 , 14 , 15 ]. In 2018, Wang et al reduced residual noise by adding the phase of the narrow-band sine wave, and proposed uniform phase empirical mode decomposition (UPEMD) [ 16 ], then Zheng et al optimized the amplitude of the sine wave added by UPEMD [ 17 ]. The decomposition effect was found to be better than CEEMDAN.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method Based on AUPLMD and RTSMWPE for a Reciprocating Compressor Valve

Song

Wang

Zhao

et al. 2022

Entropy

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In order to effectively extract the key feature information hidden in the original vibration signal, this paper proposes a fault feature extraction method combining adaptive uniform phase local mean decomposition (AUPLMD) and refined time-shift multiscale weighted permutation entropy (RTSMWPE). The proposed method focuses on two aspects: solving the serious modal aliasing problem of local mean decomposition (LMD) and the dependence of permutation entropy on the length of the original time series. First, by adding a sine wave with a uniform phase as a masking signal, adaptively selecting the amplitude of the added sine wave, the optimal decomposition result is screened by the orthogonality and the signal is reconstructed based on the kurtosis value to remove the signal noise. Secondly, in the RTSMWPE method, the fault feature extraction is realized by considering the signal amplitude information and replacing the traditional coarse-grained multi-scale method with a time-shifted multi-scale method. Finally, the proposed method is applied to the analysis of the experimental data of the reciprocating compressor valve; the analysis results demonstrate the effectiveness of the proposed method.

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Improved uniform phase empirical mode decomposition and its application in machinery fault diagnosis

Cited by 80 publications

References 20 publications

Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index

Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index

Gearbox Fault Diagnosis Based on Improved Variational Mode Extraction

Fault Diagnosis Method Based on AUPLMD and RTSMWPE for a Reciprocating Compressor Valve

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