Research on Fault Feature Extraction Method Based on FDM-RobustICA and MOMEDA

Yang, Jingzong; Li, Xuefeng; Wu, Limei

doi:10.1155/2020/6753949

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…The algorithm realizes the kurtosis optimization by using linear search and algebraic calculation of the global optimal step size. The frame of independent component analysis is shown in Figure 1 [31]. Assuming that the mixed data containing noise is X and the output signal is Y = WX, the kurtosis formula can be expressed as follows:…”

Section: Robust Independent Component Analysismentioning

confidence: 99%

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Research on Fault Feature Extraction Method Based on Parameter Optimized Variational Mode Decomposition and Robust Independent Component Analysis

Yang

Zhou

2022

Coatings

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The variational mode decomposition mode (VMD) has a reliable mathematical derivation and can decompose signals adaptively. At present, it has been widely used in mechanical fault diagnosis, financial analysis and prediction, geological signal analysis, and other fields. However, VMD has the problems of insufficient decomposition and modal aliasing due to the unclear selection method of modal component k and penalty factor α. Therefore, it is difficult to ensure the accuracy of fault feature extraction and fault diagnosis. To effectively extract fault feature information from bearing vibration signals, a fault feature extraction method based on VMD optimized with information entropy, and robust independent component analysis (RobustICA) was proposed. Firstly, the modal component k and penalty factor α in VMD were optimized by the principle of minimum information entropy to improve the effect of signal decomposition. Secondly, the optimal parameters weresubstituted into VMD, and several intrinsic mode functions (IMFs) wereobtained by signal decomposition. Secondly, the kurtosis and cross-correlation coefficient criteria were comprehensively used to evaluate the advantages and disadvantages of each IMF.And then, the optimal IMFs were selected to construct the observation signal channel to realize the signal-to-noise separation based on RobustICA. Finally, the envelope demodulation analysis of the denoised signal was carried out to extract the fault characteristic frequency. Through the analysis of bearing simulation signal and actual data, it shows that this method can extract the weak characteristics of rolling bearing fault signal and realize the accurate identification of fault. Meanwhile, in the bearing simulation signal experiment, the results of kurtosis value, cross-correlation coefficient, root mean square error, and mean absolute error are 6.162, 0.681, 0.740, and 0.583, respectively. Compared with other traditional methods, better index evaluation value is obtained.

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Section: Robust Independent Component Analysismentioning

confidence: 99%

“…The experimental data used in the experiment were from Case Western Reserve University [33]. The structure diagram of the rolling bearing test platform and rolling bearing is shown in Figure 20 [31]. The experimental platform mainly consists of the drive motor, torque speed sensor, and power meter.…”

Section: Case Analysismentioning

confidence: 99%

Research on Fault Feature Extraction Method Based on Parameter Optimized Variational Mode Decomposition and Robust Independent Component Analysis

Yang

Zhou

2022

Coatings

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“…This method uses a time target vector to determine the position and weight of the impulse and obtains the optimal filter without iteration by maximizing the multi-D-norm (MDN). Because it is effective in extracting periodic shocks, the MOMEDA method has been used for weak feature extraction [ 16 , 17 ]. However, determining the impulse repetition period a priori has always been a difficult problem in MOMEDA.…”

Section: Introductionmentioning

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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Rolling Bearing Fault Diagnosis under Strong Background Noise Based on ACMD and Optimized MOMEDA

Shi,

Wang,

Huang

et al. 2024

Journal of Sensors

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A method based on adaptive chirp mode decomposition (ACMD) and optimized multipoint optimal minimum entropy deconvolution adjusted (OMOMEDA) is proposed to diagnose the rolling bearing fault in the presence of strong background noise. First, ACMD based on the Gini index is used to separate the low resonance impulse component in the fault signal from the harmonic component and noise. After ACMD, the OMOMEDA process is performed on the low resonance impulse component to enhance the fault impulse. After OMOMEDA, the envelope analysis is conducted on the deconvoluted signal to determine the fault condition. It should be noted that OMOMEDA overcomes the parameters’ artificial dependence of multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and realizes a superior optimization solution through two aspects of improvement. First, OMOMEDA introduces the augmented gray wolf optimizer and cuckoo search to achieve adaptive determination of the period parameter. Second, OMOMEDA defines balanced permutation entropy (BPE) and uses BPE as a fitness function for finding the best filter length. The simulation and experimental results show that the proposed method can accurately and effectively diagnose the rolling bearing fault in the presence of strong background noise.

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Research on Fault Feature Extraction Method Based on FDM-RobustICA and MOMEDA

Cited by 3 publications

References 33 publications

Research on Fault Feature Extraction Method Based on Parameter Optimized Variational Mode Decomposition and Robust Independent Component Analysis

Research on Fault Feature Extraction Method Based on Parameter Optimized Variational Mode Decomposition and Robust Independent Component Analysis

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

Rolling Bearing Fault Diagnosis under Strong Background Noise Based on ACMD and Optimized MOMEDA

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