Power spectral density-guided variational mode decomposition for the compound fault diagnosis of rolling bearings

Cai, Yi; Wang, Hao; Ran, Le; Zhou, Lu; Lin, Jianhui

doi:10.1016/j.measurement.2022.111494

Cited by 32 publications

(13 citation statements)

References 39 publications

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“…Signal decomposition-based methods are similar to pattern recognition that relied on feature engineering, in which the different components are expected to be separated. Scholars and researchers have proposed lots of successful methods for compound fault diagnosis, such as Wavelet Transform (WT) [20][21][22][23][24][25][26][27][28], Variational Mode Decomposition (VMD) [29][30][31][32][33][34], Local Mean Decomposition (LMD) [35], Singular Spectrum Decomposition (SSD) [36,37], Symplectic Geometry Mode Decomposition (SGMD) [38,39], and other methods [40][41][42][43][44][45][46][47][48]. first, the compound fault signals are separated into different empirical models by empirical WT; second, a duffing oscillator which incorporates all single fault frequency is used to establish the fault isolator; finally, all the single faults can be recognized one by one by observing the chaotic motion from the Poincar mapping of the fault isolator outputs [20].…”

Section: ) Signal Decomposition-based Methodsmentioning

confidence: 99%

“…Parameters of VMD, such as the penalty and the number of subcomponents, are significant for the decomposition results. Therefore, parameteroptimized VMD has also been investigated and applied in compound fault diagnosis for rotating machinery [32][33][34]. However, it is still lacking an effective solution for determining these parameters to ensure the diagnosis performance.…”

Section: ) Signal Decomposition-based Methodsmentioning

confidence: 99%

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Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Huang

Xia

Zhang

et al. 2023

JDMD

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Compound fault, as a primary failure leading to unexpected downtime of rotating machinery, dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered in implementing compound fault diagnosis (CFD), researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years. Admittedly, many systematic surveys focused on fault diagnosis have been conducted by reputable researchers. Nevertheless, previous review articles paid more attention to fault diagnosis with several single or independent faults, resulting in that there is still lacking a comprehensive survey on CFD. Therefore, to fulfill the above requirements, it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical. Specifically, the backgrounds including the related definitions and a new taxonomy of CFD methods are detailed according to the way of implementing compound fault recognition. Then, the state-of-the-art applications of CFD are overviewed based on relevant publications in the past decades. Finally, the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey. We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.

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Section: ) Signal Decomposition-based Methodsmentioning

confidence: 99%

Section: ) Signal Decomposition-based Methodsmentioning

confidence: 99%

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Huang

Xia

Zhang

et al. 2023

JDMD

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“…32 Due to its superior performance, VMD has been improved and developed in plenty of aspects. 33 The main objective of VMD is to decompose a measured signal f into integer sub signal u k , that is, VIMFs, and the frequency bandwidth of each sub signal has a specific sparse property. In other words, assuming that there are k pulse center frequencies ω k in the input signal, VMD would decompose the relatively compact sub signal u k distributed around ω k , and the estimation of distribution range is that of frequency band range of sub signal u k .…”

Section: Adaptive Matching Extraction Strategy For Multi-fault Featuresmentioning

confidence: 99%

Harmonic spectral correlated kurtosis and an adaptive matching extraction strategy of multi-fault features for rotating machinery

Cai

Ran

Tang

et al. 2023

Structural Health Monitoring

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Rotating machinery is an important and easily damaged component in large-scale equipment. Under the coupling action of system components, the occurrence rate of compound faults is very high, which seriously endangers equipment safety. The vibration signals of the damaged rotating machine include equipment operation vibration information, periodic impacts, environmental noise, and even accidental impacts. To effectively extract multi-fault features from compound fault signals, a multi-period pulse detection indicator called harmonic spectrum correlation kurtosis (HSCK) is proposed in this paper. Based on this, an adaptive matching extraction strategy for multiple fault features is proposed. By introducing variational mode decomposition, an adaptive plane paving method for signal components is designed, and an enhanced cyclic frequency estimation method is proposed to pre-determine the fault characteristic frequency as a prior parameter of HSCK, so as to obtain the optimal center frequency and bandwidth of multiple resonance bands. The implementation of this strategy can obtain more periodic pulse information with a high signal-to-noise ratio. Simulation results show that the strategy is accurate and effective. The data of wheel-bearing compound fault and bearing multi-element compound fault indicate that the proposed strategy can be used for compound fault diagnosis of rotating machinery.

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“…Micro-earthquake data is being carried out from the frequency spectrum and the spectrogram to determine the frequency width for the filtering process [21], [22]. Aside from that, spectrogram analysis is also needed to identify the variation of harmonic signal frequency towards the time [23]. This aims to determine the frequency limit, which will be used in the filtering process [19], [23].…”

Section: B Filteringmentioning

confidence: 99%

“…Aside from that, spectrogram analysis is also needed to identify the variation of harmonic signal frequency towards the time [23]. This aims to determine the frequency limit, which will be used in the filtering process [19], [23]. The filtering uses the Butterwort band-pass filter because it is specialized for the filtering process (band-pass) [24], [25].…”

Section: B Filteringmentioning

confidence: 99%

Delay Time (Î´t) and Polarization Direction (Ï†) Analysis Based on Shear Wave Splitting (SWS) Method

Utama¹,

Garini²,

Kurnia³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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The information of dominant polarization direction and the mapping of fracture intensity are among the most important informations during the monitoring of geothermal field reservoir evaluation, as an effort to develop geothermal energy production. The appearance of geothermal reservoir fractures caused by fluid injection and the production activity resulting in the decreased pore pressure and appearance of open weak zone. The micro-earthquake activity around the area can represent these fractures that appear in the geothermal reservoir. Shear Wave Splitting (SWS) analysis can be done based on the polarization of S wave through the anisotropy medium recorded by seismograph. There are two parameters related to Shear Wave Splitting: the polarization direction (φ) related to the micro fracture direction with its delay time (δt), showing the fractures density and its permeability area. The result of Shear Wave Splitting Analysis of the field X geothermal shows that two dominant polarization directions are NW-SE and NE-SW. It is caused by the fractures around the X field geothermal with similar fractures direction, and it is compatible with the distribution microearthquake hypocenter of the previous study. Based on the map of fractures intensity, the value range shows a relatively dense intensity value around 6.6 -8.0 ms/km. The high value of intensity fractures indicates a high value of anisotropy around the area, and it is also confirming the presumption of the high permeability potential of the X geothermal field.

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Power spectral density-guided variational mode decomposition for the compound fault diagnosis of rolling bearings

Cited by 32 publications

References 39 publications

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Harmonic spectral correlated kurtosis and an adaptive matching extraction strategy of multi-fault features for rotating machinery

Delay Time (Î´t) and Polarization Direction (Ï†) Analysis Based on Shear Wave Splitting (SWS) Method

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