An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy

Li, Zhuorui; Ma, Jun; Wang, Xiaodong; Li, Xiang

doi:10.3390/s21020533

Cited by 11 publications

(8 citation statements)

References 41 publications

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“…The inappropriate selection of these parameters leads to erroneous results. In addition, MKCD extracts only seven pulses which reduces the diagnostic efficiency [50]. Therefore, Geoff L [37] modified MKCD algorithm by introducing deconvolution to extract multiple pulses and termed it MOMEDA.…”

Section: Multipoint Optimal Minimum Entropy Deconvolution Adjusted (M...mentioning

confidence: 99%

Adaptive MOMEDA model based variational mode decomposition for Pelton wheel fault detection

Vashishtha¹,

Chauhan²,

Yadav³

et al. 2022

Eng. Res. Express

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A critical step in fault diagnosis is determining the frequency of faults. Variational mode decomposition (VMD) is extensively employed for this purpose since it can describe the signal in the time-frequency domain. On the other hand, the VMD frequently fails to analyse non-stationary data containing low-frequency disturbances/noises. A multipoint optimal minimal entropy deconvolution adjusted (MOMEDA) is used with VMD in this research to improve defect detection performance in the presence of low-frequency disturbances. The filter length has a strong influence on the output of MOMEDA thus choosing the right one is a critical step in recovering a periodic pulse in the event of a weak defective signal. Improved grey wolf optimization (GWO) adaptively selects the appropriate filter length using the autocorrelation energy as its fitness function. The GWO is improved by introducing a gaussian mutation strategy which maintains the proper balance between the exploration and exploitation process. The proposed method has been applied to investigate the bucket defects of the Pelton wheel. The raw vibration signal is first decomposed into a series of modes using VMD. Second, the MOMEDA model is used to purify each mode by reducing low-frequency noise interference. The modes processed by MOMEDA are reconstructed again into a pure signal. Finally, the Hilbert envelop spectrum of the pure signal is obtained to determine the fault frequency. The same is verified from the theoretical fault feature frequency of the turbine bucket. The performance of the proposed method in extracting fault frequency accurately is also compared with other two models: a) MOMEDA model with ensemble empirical mode decomposition (EEMD) and b) MOMEDA model with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The comparison results proved the efficacy and superiority of the proposed method.

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Section: Multipoint Optimal Minimum Entropy Deconvolution Adjusted (M...mentioning

confidence: 99%

Adaptive MOMEDA model based variational mode decomposition for Pelton wheel fault detection

Vashishtha¹,

Chauhan²,

Yadav³

et al. 2022

Eng. Res. Express

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show abstract

“…In order to achieve this goal, a target vector t composed of constants is constructed according to the signal period to describe the weight and position of the impact pulse [ 28 ]. Then, multipoint D-norm (MDN) is defined to reflect the impulse characteristics of the filtered signal:

…”

Section: Basic Theory Of the Proposed Techniquementioning

confidence: 99%

Application of Adaptive MOMEDA with Iterative Autocorrelation to Enhance Weak Features of Hoist Bearings

Kou

et al. 2021

Entropy

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Low-speed hoist bearings are characterized by fault features that are weak and difficult to extract. Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is an effective method for extracting periodic pulses in a signal. However, the decomposition effect of MOMEDA largely depends on the selected pulse period and filter length. To address these drawbacks of MOMEDA and accurately extract features from the vibration signal of a hoist bearing, an adaptive feature extraction method is proposed based on iterative autocorrelation (IAC) and MOMEDA. To automatically identify the pulse period, a new evaluation index named autocorrelation kurtosis entropy (AKE) was constructed to select the optimal IAC. To eliminate the influence of the filter length on the decomposition effect, an iterative MOMEDA strategy was designed to gradually enhance signal impulse features. The Case Western Reserve University bearing dataset and bearing data from a self-made hoisting test setup were used to verify the effectiveness of IAC-MOMEDA in extracting weak features. Moreover, the capability of IAC-MOMEDA for features extraction of normal bearing vibration signal was further confirmed by field test data.

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“…Another family of optimization strategy is the optimized deconvolution methods. In comparison to optimized MD methods, the optimized deconvolution methods have two obvious advantages [15,16]: (1) can not only eliminate background noise and vibration interferences but also simultaneously highlight the fault-induced periodic impulsive components and (2) avoid the significant computational burden. Essentially, the optimized deconvolution methods are specifically tailored to recover impulse-like signals, making them more suitable for analyzing bearing fault impact signals compared with the optimized WT and optimized MD methods.…”

Section: Introductionmentioning

confidence: 99%

An optimized Laplacian of Gaussian filter using improved sparrow search algorithm for bearing fault extraction

Feng,

Yang,

Wei

2023

Meas. Sci. Technol.

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Precise detection of fault characteristics in rolling bearings is imperative for machine health management. However, due to the presence of interfering components including noise and periodic components caused by vibration sources, the extraction of weak fault-related information cannot be achieved precisely. In this study, we propose an optimized Laplacian of Gaussian (LoG) filtering technique to handle this issue. The proposed algorithm utilizes the envelope entropy and Gini of square envelope as an objection function to optimize two important parameters, namely standard deviation and filter order of the LoG filter, through an improved sparrow search algorithm (SSA) named adaptive spiral flying SSA. Afterward, the LoG filtering method with the optimal parameters is employed to filter the raw vibration data. Finally, the filtered signal undergoes envelope analysis for fault feature detection. A simulated test and two case studies demonstrate the effectiveness and superiority of the LoG technique.

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An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy

Cited by 11 publications

References 41 publications

Adaptive MOMEDA model based variational mode decomposition for Pelton wheel fault detection

Adaptive MOMEDA model based variational mode decomposition for Pelton wheel fault detection

Application of Adaptive MOMEDA with Iterative Autocorrelation to Enhance Weak Features of Hoist Bearings

An optimized Laplacian of Gaussian filter using improved sparrow search algorithm for bearing fault extraction

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