Multiscale envelope manifold for enhanced fault diagnosis of rotating machines

Zhu, Zhongkui; He, Qingbo; Kong, Fanrang

doi:10.1016/j.ymssp.2014.07.021

Cited by 28 publications

(17 citation statements)

References 32 publications

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“…The complex Morlet wavelet is a harmonic wave with a center frequency, f c , multiplied by a Gaussian time domain window, and mathematically can be defined as [35,36]:…”

Section: Smoothed Envelope Generationmentioning

confidence: 99%

Automatic gear and bearing fault localization using vibration and acoustic signals

Jena

Panigrahi

2015

Applied Acoustics

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“…The complex Morlet wavelet is a harmonic wave with a center frequency, f c , multiplied by a Gaussian time domain window, and mathematically can be defined as [35,36]:…”

Section: Smoothed Envelope Generationmentioning

confidence: 99%

Automatic gear and bearing fault localization using vibration and acoustic signals

Jena

Panigrahi

2015

Applied Acoustics

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“…The commonly used time-domain indicators include maximum, minimum, mean, mean square root and kurtosis value [7]. Frequency domain analysis such as spectrum and envelope analysis is the most involved method in the mechanical fault diagnosis [8]. However, analysis only relying on time domain or frequency domain cannot meet the needs of the current mechanical fault diagnosis, time-frequency analysis has become a hot research topic [9].…”

Section: Introductionmentioning

confidence: 99%

A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

Zhang

Jiang

et al. 2019

Chin. J. Mech. Eng.

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Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.

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“…Because noise plays a negative role in most cases, traditional methods mostly focus on filtering out noise. However, early fault signals tend to be weak, so filtering of useful signals can easily occur in the process of filtering out noise, which is very unfavorable to the detection results [13,14]. In fact, noise is not harmful in all cases, and sometimes, it can enhance the strength of the useful signal, which is conducive to signal extraction and detection.…”

Section: Introductionmentioning

confidence: 99%

Time‐Delayed Feedback Tristable Stochastic Resonance Weak Fault Diagnosis Method and Its Application

Han

Wang

et al. 2019

Shock and Vibration

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Pulses caused by rotating mechanical faults are weak and often submerged in strong background noise, which can affect the accuracy of fault detection. To solve this problem, we study the stochastic resonance phenomenon of a tristable potential system based on strong noise background and also investigate the influence of time-delayed feedback on this stochastic resonance model. The effects of time-delayed feedback strength on potential energy, steady-state probability density function, and signal-to-noise ratio (SNR) are discussed. The results show that stochastic resonance can be enhanced or suppressed by adjusting the delay time and feedback strength. Combined with bearing fault diagnosis simulation research and experimental verification evaluation, the proposed time-delayed feedback tristable stochastic resonance fault diagnosis method is more effective than the classical stochastic resonance method.

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Multiscale envelope manifold for enhanced fault diagnosis of rotating machines

Cited by 28 publications

References 32 publications

Automatic gear and bearing fault localization using vibration and acoustic signals

Automatic gear and bearing fault localization using vibration and acoustic signals

A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

Time‐Delayed Feedback Tristable Stochastic Resonance Weak Fault Diagnosis Method and Its Application

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