Improved double TQWT sparse representation using the MQGA algorithm and new norm for aviation bearing compound fault detection

Zhang, Shuo; Liu, Zhiwen; He, Sihai; Wang, Jinglin; Chen, Lufeng

doi:10.1016/j.engappai.2022.104741

Cited by 21 publications

(4 citation statements)

References 30 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%

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%

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

Huang

Xia

Zhang

et al. 2023

JDMD

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“…ey are not only complex in structure but also have a strong correlation with internal parts [1]. At the same time, the operating environment is very complex.…”

Section: Introductionmentioning

confidence: 99%

Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed Conditions

Huang

Xu²,

Cao³

et al. 2022

Shock and Vibration

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Fault diagnosis of rolling bearings under variable speed is a common issue in engineering practice, but it lacks an effective diagnosis algorithm, while approaches developed for steady speed cannot be directly applied. Therefore, for effectively identifying bearing faults under variable speed, this paper proposed a multiscale fractional dimensionless indicator (MSFDI) and put forward a fault diagnosis method with random forest (RF). It can overcome the feature space aliasing problem of traditional dimensionless indicators, which will lead to increased diagnosis uncertainty. The multiorder fractional Fourier transform is carried out on bearing signals to get a series of fractional Fourier domain components, which will be used to construct the original MSFDI feature set. Moreover, reliefF selects the sensitive MSFDIs as the input of the RF algorithm to determine the health condition. The effectiveness of the proposed method is verified by experiments and case studies.

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“…Compared to the time-frequency signal caused by one fault, that by multiple faults interfere with each other, leading to coupling phenomena. Especially when multiple faults are not uniformly distributed among the raceway, the time-frequency signal become increasingly complicated and harder to identify (Wan et al, 2018; Xue et al, 2020; Zhang et al, 2022). Therefore, the diagnosis of multiple faults is a critical research field.…”

Section: Introductionmentioning

confidence: 99%

Joint time-frequency distribution of rolling bearings with multiple faults on outer raceway based on asymptotic model

Tao

et al. 2022

Journal of Vibration and Control

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At present, the majority of the research on dynamic model of rolling bearings with multiple faults is conducted on the assumption that the displacement deviation between raceway and rolling element changes instantaneously. However, the deviation should vary gradually. As a result, an asymptotic model of rolling bearings with multiple faults on outer raceway is established, considering the interaction of multiple faults. Synchroextracting transformation method is employed to describe the joint time-frequency distribution of the faulty rolling bearings. The discrepancies between the asymptotic model and the traditional model are contrasted and analyzed via simulation modeling. Simultaneously, the effects of fault number and fault interval on the joint time-frequency distribution are investigated. Finally, experiments are performed to verify the rationality of the established model. Simulation results demonstrate that the joint time-frequency distribution derived by the asymptotic model includes not only the fault characteristic frequency but also the rotating frequency. The central frequency increases in a multiple of the fault characteristic frequency as the fault number increase, as does its energy amplitude. Only when multiple faults are uniformly distributed among the interval range of rolling elements, the energy amplitude of the central frequency can reach the maximum. The energy amplitude of the rotating frequency scarcely changes as the fault interval and fault number vary.

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Improved double TQWT sparse representation using the MQGA algorithm and new norm for aviation bearing compound fault detection

Cited by 21 publications

References 30 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

Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed Conditions

Joint time-frequency distribution of rolling bearings with multiple faults on outer raceway based on asymptotic model

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