Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm

Zhou, Jie; Guo, Xiaoming; Wang, Zhijian; Du, Wenhua; Wang, Junyuan; Han, Xiaofeng; Jingtai, Wang; He, Gaofeng; He, Huihui; Xue, Huiling; Kou, Yanfei

doi:10.3390/e21040400

Cited by 16 publications

(8 citation statements)

References 44 publications

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“…Permutation entropy (PE) is proposed by Bandt et al, and it is a method to judge the randomness and complexity of time series [ 37 ]. It has small calculation and strong anti-interference ability, and it is applied in many fields as fitness function or feature selection [ 38 , 39 ]. The specific introduction of PE is as follows:…”

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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“…The amplitude is also larger than the amplitude of ARMA-MED, and the noise in the envelope spectrum is much smaller than that of ARMA-MED, which shows that the effect is better than that of ARMA-MED. To elaborate on the performance advantages of the proposed method, we use permutation entropy [45] and the fault energy ratio [46] to compare several methods from time to time and across the frequency domain. The comparison results are shown in Table 1.…”

Section: Simulation Verificationmentioning

confidence: 99%

“…ARMA, autoregressive moving average; MOMEDA, multipoint optimal minimum entropy deconvolution adjusted. To elaborate on the performance advantages of the proposed method, we use permutation entropy [45] and the fault energy ratio [46] to compare several methods from time to time and across the frequency domain. The comparison results are shown in Table 1.…”

Section: Simulation Verificationmentioning

confidence: 99%

Application of a New Enhanced Deconvolution Method in Gearbox Fault Diagnosis

Wang

Jingtai

et al. 2019

Applied Sciences

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When the mechanical transmission mechanism fails, such as gears and bearings in the gearbox, its vibration signal often appears as a periodic impact. Considering the influence of noise, however, the fault signal is often submerged in the noise, so it is necessary to propose a feasible and effective fault extraction method. MOMEDA (multipoint optimal minimum entropy deconvolution adjusted) overcomes the tedious iterative process of MED (minimum entropy deconvolution) and overcomes the resampling trouble in MCKD (maximum correlated kurtosis deconvolution). It is suitable for dealing with periodic impact signal. Besides, aiming at the poor ability of MOMEDA to capture the deconvolution result of target function in a strong noise environment, this paper proposes an improved MOMEDA gearbox fault feature extraction method. Considering that MOMEDA has poor anti-noise performance and can easily cause misdiagnosis in a strong noisy environment, this paper constructs an autoregressive mean sliding model to improve the noise immunity of MOMEDA. Firstly, the stability of the test signal is judged by the autocorrelation coefficient (ACF) and the partial correlation coefficient (PACF). Secondly, the ARMA (autoregressive moving average) model is constructed and a set of optimal model coefficients are obtained to filter the signal, which greatly improves MOMEDA’s ability to capture fault features. Thirdly, the fault feature is extracted by MOMEDA, and the fault information is extracted accurately under a strong noise environment. Finally, compared with AR-MED, ARMAMED, and other methods, the advantages of ARMAMOMEDA are verified. Moreover, the effectiveness and superiority of the proposed method are verified by simulation signals and experimental data from the Case Western Reserve University Bearing Data Center.

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“…Wang et al [43] adopted multi-objective particle swarm optimization with symbol dynamic entropy to select automatically the two parameters of VMD. Zhou et al [44] applied the immunized fruit fly optimization algorithm containing the objective function named permutation entropy to determine the combined parameters of VMD. 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.…”

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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Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm

Cited by 16 publications

References 44 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

Application of a New Enhanced Deconvolution Method in Gearbox Fault Diagnosis

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

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