Identification of mechanical compound-fault based on the improved parameter-adaptive variational mode decomposition

Miao, Yongwu; Zhao, Ming; Lin, Jing

doi:10.1016/j.isatra.2018.10.008

Cited by 199 publications

(106 citation statements)

References 48 publications

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“…Compared with the EMD algorithm, the VMD algorithm has a clearer mathematical theory and better noise robustness [ 32 ]. Because of the advantages of the VMD algorithm, the VMD algorithm is widely used in many fields [ 33 , 34 , 35 ].…”

Section: Methodsmentioning

confidence: 99%

A Denoising Method for Fiber Optic Gyroscope Based on Variational Mode Decomposition and Beetle Swarm Antenna Search Algorithm

Wang¹,

Gao²,

Wu³

et al. 2020

Entropy

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Fiber optic gyroscope (FOG) is one of the important components of Inertial Navigation Systems (INS). In order to improve the accuracy of the INS, it is necessary to suppress the random error of the FOG signal. In this paper, a variational mode decomposition (VMD) denoising method based on beetle swarm antenna search (BSAS) algorithm is proposed to reduce the noise in FOG signal. Firstly, the BSAS algorithm is introduced in detail. Then, the permutation entropy of the band-limited intrinsic mode functions (BLIMFs) is taken as the optimization index, and two key parameters of VMD algorithm, including decomposition mode number K and quadratic penalty factor α , are optimized by using the BSAS algorithm. Next, a new method based on Hausdorff distance (HD) between the probability density function (PDF) of all BLIMFs and that of the original signal is proposed in this paper to determine the relevant modes. Finally, the selected BLIMF components are reconstructed to get the denoised signal. In addition, the simulation results show that the proposed scheme is better than the existing schemes in terms of noise reduction performance. Two experiments further demonstrate the priority of the proposed scheme in the FOG noise reduction compared with other schemes.

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Section: Methodsmentioning

confidence: 99%

A Denoising Method for Fiber Optic Gyroscope Based on Variational Mode Decomposition and Beetle Swarm Antenna Search Algorithm

Wang¹,

Gao²,

Wu³

et al. 2020

Entropy

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“…Wei et al [45] proposed a whale optimization algorithm-based optimal VMD where envelope entropy is regarded as the objective function to find two optimal parameters of VMD. Miao et al [46] presented an improved parameter-adaptive VMD, which can efficiently obtain two key parameters of VMD by using a grasshopper optimization algorithm containing the ensemble kurtosis. Although the objective function adopted in the above research separately investigates the impact properties of signals and the correlation between signals, it does so without considering simultaneously the impacting characteristics and correlation of signals, which indicates that the decomposition results of VMD may not be optimal.…”

Section: Introductionmentioning

confidence: 99%

Research on a Novel Improved Adaptive Variational Mode Decomposition Method in Rotor Fault Diagnosis

et al. 2020

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Variational mode decomposition (VMD) with a non-recursive and narrow-band filtering nature is a promising time-frequency analysis tool, which can deal effectively with a non-stationary and complicated compound signal. Nevertheless, the factitious parameter setting in VMD is closely related to its decomposability. Moreover, VMD has a certain endpoint effect phenomenon. Hence, to overcome these drawbacks, this paper presents a novel time-frequency analysis algorithm termed as improved adaptive variational mode decomposition (IAVMD) for rotor fault diagnosis. First, a waveform matching extension is employed to preprocess the left and right boundaries of the raw compound signal instead of mirroring the extreme extension. Then, a grey wolf optimization (GWO) algorithm is employed to determine the inside parameters ( α ^ , K) of VMD, where the minimization of the mean of weighted sparseness kurtosis (WSK) is regarded as the optimized target. Meanwhile, VMD with the optimized parameters is used to decompose the preprocessed signal into several mono-component signals. Finally, a Teager energy operator (TEO) with a favorable demodulation performance is conducted to efficiently estimate the instantaneous characteristics of each mono-component signal, which is aimed at obtaining the ultimate time-frequency representation (TFR). The efficacy of the presented approach is verified by applying the simulated data and experimental rotor vibration data. The results indicate that our approach can provide a precise diagnosis result, and it exhibits the patterns of time-varying frequency more explicitly than some existing congeneric methods do (e.g., local mean decomposition (LMD), empirical mode decomposition (EMD) and wavelet transform (WT) ).

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“…Lyu et al [ 16 ] proposed an improved maximum correlated kurtosis deconvolution method based on quantum genetic algorithm to diagnose compound fault. Miao et al [ 17 ] developed an improved parameter-adaptive variational mode decomposition for identification of compound fault. Pan et al [ 18 ] utilized symplectic geometry mode decomposition to decompose complex signal and got a good result.…”

Section: Introductionmentioning

confidence: 99%

Compound Fault Diagnosis of Rolling Bearing Based on Singular Negentropy Difference Spectrum and Integrated Fast Spectral Correlation

Tang

Tian

2020

Entropy

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Compound fault diagnosis is challenging due to the complexity, diversity and non-stationary characteristics of mechanical complex faults. In this paper, a novel compound fault separation method based on singular negentropy difference spectrum (SNDS) and integrated fast spectral correlation (IFSC) is proposed. Firstly, the original signal was de-noised by SNDS which improved the noise reduction effect of singular difference spectrum by introducing negative entropy. Secondly, the de-noised signal was analyzed by fast spectral correlation. Finally, IFSC took the fourth-order energy as the index to determine the resonance band and separate the fault features of different single fault. The proposed method is applied to analyze the simulated compound signals and the experimental vibration signals, the results show that the proposed method has excellent performance in the separation of rolling bearing composite faults.

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Identification of mechanical compound-fault based on the improved parameter-adaptive variational mode decomposition

Cited by 199 publications

References 48 publications

A Denoising Method for Fiber Optic Gyroscope Based on Variational Mode Decomposition and Beetle Swarm Antenna Search Algorithm

A Denoising Method for Fiber Optic Gyroscope Based on Variational Mode Decomposition and Beetle Swarm Antenna Search Algorithm

Research on a Novel Improved Adaptive Variational Mode Decomposition Method in Rotor Fault Diagnosis

Compound Fault Diagnosis of Rolling Bearing Based on Singular Negentropy Difference Spectrum and Integrated Fast Spectral Correlation

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