Compound Bearing Fault Detection Under Varying Speed Conditions With Virtual Multichannel Signals in Angle Domain

Tang, Gang; Wang, Yatao; Huang, Yujing; Liu, Ning; He, Jianyun

doi:10.1109/tim.2020.2965634

Cited by 57 publications

(19 citation statements)

References 38 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%

“…Besides the aforementioned methods, there exist many other methods to make a compound fault diagnosis by combining the signal decomposition algorithm with other techniques. For instance, Tang et al proposed a compound fault detection method with virtual multichannel signals in the angel domain and applied it to monitoring the rolling bearings under varying working conditions [43]. More details can be found in [40][41][42][43][44][45][46][47][48], which are not enumerated here.…”

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

View full text Add to dashboard Cite

show abstract

“…Qin et al developed a rede ned dimensionless indicator according to VMD linked with grid search and support vector machine (SVM) [10]. However, such methods are established on the premise of steady speed operating conditions and need to be combined with computing order tracking (COT) to be used in variable speed conditions, which require synchronous speed information [11]. Fractional Fourier transform (FrFT) extends the frequency domain obtained by using traditional FT to the broader time-frequency domain by adjusting the FrFT rotation angle [12].…”

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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“…Rolling bearings are one of the most critical components in rotating machinery and their operating state will affect the overall performance of mechanical equipment [1][2][3]. Bearing performance degradation trend prognosis can ensure the stability of mechanical equipment, avoid catastrophic events, and extend the life cycle of the machinery [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Multiscale Deep Health Indicator with Bidirectional LSTM Network for Bearing Performance Degradation Trend Prognosis

Wang

Tang

Zhou

et al. 2020

Shock and Vibration

Self Cite

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As rolling bearings are the key components in rotating machinery, bearing performance degradation directly affects machine running status. A tendency prognosis for bearing performance degradation is thus required to ensure the stability of operation. This paper proposes a novel strategy for bearing performance degradation trend prognosis, including health indicator construction techniques and a performance degradation trend prediction method. To more accurately represent the degradation trend, the multiscale deep bottleneck health indicator is proposed as a new synthesized health indicator to remove high-frequency detail signals from features, which can reduce possible fluctuations in conventional synthetic health indicators. A suitable method for selecting the statistical characteristics required for fusion is also presented to solve the problem of information redundancy that affects trend representation. In addition, a stacked autoencoder network is used for deep feature extraction of selected statistical features. A bidirectional long short-term memory network prediction model is also proposed for the prediction of degradation trend, which can make full use of historical and future information to improve prediction accuracy. Finally, experiments are carried out to verify the effectiveness of the proposed method.

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Compound Bearing Fault Detection Under Varying Speed Conditions With Virtual Multichannel Signals in Angle Domain

Cited by 57 publications

References 38 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

A Novel Multiscale Deep Health Indicator with Bidirectional LSTM Network for Bearing Performance Degradation Trend Prognosis

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