Hurst based vibro-acoustic feature extraction of bearing using EMD and VMD

Mohanty, Satish; Gupta, Karunesh Kumar; Raju, Kota Solomon

doi:10.1016/j.measurement.2017.12.012

Cited by 145 publications

(57 citation statements)

References 39 publications

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“…Tollis et al 8 and Mohanty et al 9 have used the HHT method to diagnose the failure of rolling bearings. 6,7 However, EMD has endpoint effect and mode aliasing problem, and sampling frequency has a greater impact on it, 10 which makes the diagnosis results prone to misdiagnosis. Aiming at the problems of EMD, a noise-assisted analysis method-ensemble empirical mode decomposition (EEMD) is proposed.…”

Section: Introductionmentioning

confidence: 99%

Composite fault diagnosis of gearbox based on empirical mode decomposition and improved variational mode decomposition

Wang

2020

Journal of Low Frequency Noise, Vibration and Active Control

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In order to identify the nonlinear nonstationary pitting-wear fault signal of gears in gearbox, a new method of composite fault diagnosis for gearbox is proposed, which combines empirical mode decomposition with improved variational mode decomposition. Aiming at the problem of false modes in the results of empirical mode decomposition when processing signals, the energy method is used to eliminate and update the mode components, and the correlation coefficient method is used to calculate the correlation between the updated mode components and the original signal. The components with strong correlation are selected to form the combined mode components to weaken the noise and improve the signal-to-noise ratio. Aiming at the problem that variational mode decomposition method needs to manually determine the number of mode components k and the penalty factor a during signal decomposition, a combination of envelope spectral entropy and waveform method is proposed to determine the optimal parameter combination. By analyzing the pitting-wear composite fault vibration signals of gears in the gearbox and the normal signals of the gearbox, the effectiveness of the proposed method is verified, and a comparative analysis with the empirical mode decomposition method is performed to highlight the superiority of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Composite fault diagnosis of gearbox based on empirical mode decomposition and improved variational mode decomposition

Wang

2020

Journal of Low Frequency Noise, Vibration and Active Control

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“…Furthermore, it is affected by the sampling frequency, and the decomposition error sometimes is large. In order to avoid these problems, a new adaptive signal processing method, VMD [9,10], is proposed. This method is used to search the optimal solution of the variational model by iterative search to determine the frequency center and bandwidth of each decomposition component.…”

Section: Introductionmentioning

confidence: 99%

A fault feature extraction method for single-channel signal of rotary machinery based on VMD and KICA

Sun¹,

Fang²,

Zhao³

2019

J. vibroeng.

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A feature extraction method combined with variational mode decomposition (VMD) and kernel independent component analysis (KICA) is proposed to improve the fault feature extraction of vibration signal of rotary machinery. Firstly, VMD is used to decompose the single-channel vibration signal. Secondly, calculate the correlation coefficient between each component and the original signal. Finally, a new multidimensional observation signal is formed with high correlation components, and the fault signals will be extracted from the new observation signal by KICA. Compared with some typical fault feature extraction methods, the better performance of the proposed method is demonstrated by two experiments which are faulty rolling bearing experiment and a comprehensive experiment with faulty rolling bearing and faulty gear. Furthermore, an experiment of faulty rotary shaft verifies the effectiveness of this method. The results demonstrate that the proposed method is efficient for fault feature extraction of single-channel vibration signal of rotary machinery.

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“…Previous researchers have used vibration analysis in order to diagnose the defect present in the wind turbine gearbox and further quantified the severity level using various machine learning algorithms [3,4]. As the acquired vibration signatures are noisy and have non-linear nature, recent studies are more concerned about the implementation of various signal processing algorithms such as wavelet transform, empirical mode decomposition to extract the fault sensitive information from the acquired data [3,5].…”

Section: Introductionmentioning

confidence: 99%

PCA based health indicator for remaining useful life prediction of wind turbine gearbox

Praveen¹,

Shah²,

Pandey³

et al. 2019

Vib. proced.

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Fault prognosis of wind turbine gearbox has received considerable attention as it predicts the remaining useful life which further allows the scheduling of maintenance strategies. However, the studies related towards the RUL prediction of wind turbine gearbox are limited, because of the complexity of gearbox, acute changes in the operating conditions and non-linear nature of the acquired vibration signals. In this study, a health indicator is constructed in order to predict the remaining useful life of the wind turbine gearbox. Run to fail experiments are performed on a laboratory scaled wind turbine gearbox of overall gear ratio 1:100. Vibration signals are acquired and decomposed through continuous wavelet transform to obtain the wavelet coefficients. Various statistical features are computed from the wavelet coefficients which return form high-dimensional input feature set. Principal component analysis is performed to reduce the dimensionality and principal components (PCs) are computed from the input feature set. PC1 is considered as the health indicator and subjected to further smoothening by linear rectification technique. Exponential degradation model is fit to the considered health indicator and the model is able to predict the RUL of the gearbox with an error percentage of 2.73 %.

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Hurst based vibro-acoustic feature extraction of bearing using EMD and VMD

Cited by 145 publications

References 39 publications

Composite fault diagnosis of gearbox based on empirical mode decomposition and improved variational mode decomposition

Composite fault diagnosis of gearbox based on empirical mode decomposition and improved variational mode decomposition

A fault feature extraction method for single-channel signal of rotary machinery based on VMD and KICA

PCA based health indicator for remaining useful life prediction of wind turbine gearbox

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