Early Fault Diagnosis of Rolling Bearing based Empirical Wavelet Transform and Spectral Kurtosis

Bai, Lin; Xi, Wei

doi:10.1109/icphm.2018.8448997

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Figure 10 shows the comparison of the classification ability of our SVM classifier and several other classifiers based on the characteristics of IMPE in this paper. Among them, CO-PNN [42] is the improved coyote optimization algorithm based probabilistic neural network which get the accuracy of 94.26%. e method of 2D-CNN [43] get the accuracy of 95.31%.…”

Section: Impe Feature Extractionmentioning

confidence: 99%

Bearing Early Fault Diagnosis Based on an Improved Multiscale Permutation Entropy and SVM

Jiang

Dai

Shao

et al. 2022

Shock and Vibration

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Bearing fault is a process of gradual development and deepening. In the early stage of the fault, if it can be found out in time and taken reasonable prevention and elimination measures, we can avoid serious losses and safety accidents. Therefore, the feature extraction and analysis of early weak fault has important practical significance. In this paper, an improved multiscale permutation entropy (IMPE) method was proposed to overcome the shortcomings in the coarse-grained process. In order to solve the problem that only considering a single coarse-grained sequence under a certain scale may lead to the loss of feature information, this paper proposed to calculate the time series with equal overlapping segments, that was to consider all coarse-grained sequences under the same scale to reflect the feature information of fault signals more comprehensively. In order to solve the problem that feature extraction is not refined enough when using the first-order moment mean value calculation in traditional MPE calculation, a calculation method based on the skewness of the third-order moment was proposed. The calculation method is more sensitive to the complexity and fluctuation of signals and can better describe the feature details and extract the fault features effectively. IMPE was applied to feature extraction of early weak fault of rolling bearing and input into Support Vector Machines (SVMs) for faults classification. Aiming at SVM parameter optimization problem, an improved chaos firefly optimization algorithm was proposed. Experimental results show that the new method of early weak fault identification based on IMPE-SVM was effective in detecting rolling bearing faults with different severity.

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Section: Impe Feature Extractionmentioning

confidence: 99%

Bearing Early Fault Diagnosis Based on an Improved Multiscale Permutation Entropy and SVM

Jiang

Dai

Shao

et al. 2022

Shock and Vibration

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show abstract

“…CLNGO was used to optimize the FMD and MNAD algorithms, and the parameter is set to the filter number 8 K = [5] (the recommended value is [5,10]), the filter length L , and the number of decomposition modes n were [30,100] and [1,7] (K > n), respectively. The parameter noise ratio ρ and filter length L of MNAD were [0.1, 0.9] and [200, 1000], respectively.…”

Section: Experimental Studymentioning

confidence: 99%

“…In engineering problems, the signal is preprocessed first to eliminate the noise in the original signal or to enhance the fault characteristic information, and then an envelope analysis can determine whether the fault characteristic frequency is included. Many classical methods have been applied to signal preprocessing, such as empirical mode decomposition (EMD) [6], wavelet transform (WT) [7], Fourier transform (FFT) [8], etc., which can highlight the periodic pulse of fault signals and reduce the difficulty of feature extraction. For example, Dragomiretskiy et al [9] proposed the variated mode decomposition (VMD) algorithm to separate noise from other different types of information in signals.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Composite Fault Diagnosis of Rolling Bearings Based on the CLNGO Algorithm

2022

Processes

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In this paper, a novel composite fault diagnosis method combining adaptive feature mode decomposition (FMD) and minimum noise amplitude deconvolution (MNAD) is proposed. Firstly, chaos mapping and leader mutation selection strategy were introduced to improve the Northern Goshawk algorithm (NGO), and a chaotic leadership Northern Goshawk optimization (CLNGO) algorithm was proposed. The advantages of the CLNGO algorithm in convergence accuracy and speed were verified by 12 benchmark functions. Then, a new index called sparse pulse and cyclicstationarity (SPC) is proposed to evaluate signal sparsity. Finally, SPC is used as the fitness function of CLNGO to optimize FMD and MNAD. The optimal decomposition mode n and filter length of FMD, and filter length L and noise ratio ρ of MNAD are selected. The CLNGO-FMD is used to decompose signal into different modes. The signal is reconstructed based on the kurtosis criterion and the CLNGO-MNAD method is used to reduce the noise of the reconstructed signal twice. The experimental results show that the proposed method can achieve the enhancement of weak features and the removal of noise to extract the fault feature frequency adaptively. Compared with EMD, VMD, MOMEDA, MCKD and other methods, the proposed method has better performance in fault feature frequency extraction, and it is effective for the diagnosis of single faults and composite faults.

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Rotating Machinery Fault Diagnosis Based on Typical Resonance Demodulation Methods: A Review

Liu

2023

IEEE Sensors J.

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Early Fault Diagnosis of Rolling Bearing based Empirical Wavelet Transform and Spectral Kurtosis

Cited by 5 publications

References 23 publications

Bearing Early Fault Diagnosis Based on an Improved Multiscale Permutation Entropy and SVM

Bearing Early Fault Diagnosis Based on an Improved Multiscale Permutation Entropy and SVM

Adaptive Composite Fault Diagnosis of Rolling Bearings Based on the CLNGO Algorithm

Rotating Machinery Fault Diagnosis Based on Typical Resonance Demodulation Methods: A Review

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