Vibration health monitoring of rolling bearings under variable speed conditions by novel demodulation technique

Zhao, Dongfeng; Gelman, Len; Chu, Fulei; Ball, Andrew

doi:10.1002/stc.2672

Cited by 24 publications

(20 citation statements)

References 41 publications

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“…Laval_2021 [76] Zhao_2021 [77] Cai_2018 [78] Sheen_2004 [79] Laval_2021 [76] Continuous vibration separation (CVS)…”

Section: Wireless Sensor Network (Wsn)mentioning

confidence: 99%

“…The demodulation issue is addressed by Laval et al in [76], who discuss the impact of a limited spectral bandwidth filtering with Hilbert demodulation, evaluating the interactions between the amplitude and phase estimations. Zhao et al in [77] propose an optimizationbased demodulation transform based on an optimal demodulation operator (DO), which allows to transform the time-varying frequency component into a constant one so that the largest peak can be detected in the spectrum of the demodulated signal. This method is used for the rolling bearing fault characteristic frequency (FCF) estimation for diagnostic aims.…”

Section: Multiple Sourcesmentioning

confidence: 99%

“…In the table, the nomenclature for categories and sub-categories of phases, components, and topics reflect the previously introduced legend. Bajaj_2022 [276] x p3 x Xu_2022 [277] x p4 x x Ye_2021a [244] x p3 x Ahmed_2021 [172] x x p2 x x Mufazzal_2021 [278] x p4 x Yang_2021 [110] x p2 x x x x x Moghadam_2021 [193] x p2 x x Saucedo-Dorantes_2021 [173] x p2 x x x x Espinoza-Sepulveda_2021 [279] x p4 x x Kalista_2021 [73] x p1 x x x Zhang_2021a [280] x p4 x x Zhang_2021b [267] x p3 x x Meng_2021 [59] x p1 x x Tiwari_2021 [95] x p1 x x Tatsis_2021 [281] x p4 x x Leaman_2021 [282] x p5 x x Ou_2021 [283] x p5 x x Espinoza_2021 [226] x x p3 x x Wang_2021 [233] x p3 x x x Goyal_2021 [60] x p1 x x Bai_2021a [203] x p2 x x x Yu_2021 [75] x p1 x x Papathanasopoulos_2021 [66] x p1 x x Sharma_2021 [25] x p1 x x x Rauber_2021 [219] x p3 x x Laval_2021 [76] x p1 x x x x Shao_2021 [158] x x p2 x x Rafiq_2021 [166] x p2 x x x x Zhao_2021 [77] x p1 x x Gómez_2021 [284] x p1 x x Jablon_2021 [197] x p2 x x Barusu_2021 [62] x p1 x x x x Hadroug_2021 [250] x p3 x Hou_2021 [35] x x p1 x x Yuan_2021 [285] x p5 x Ye_2021b [245] x p3 x x x Tingarikar_2021 [286] x p4 x Ribeiro_2021 [287] x p2 x Peng_2021 [288] x x p2 x x x Gu_2021 …”

Section: Appendix Amentioning

confidence: 99%

See 2 more Smart Citations

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

et al. 2022

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Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas.

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“…Laval_2021 [76] Zhao_2021 [77] Cai_2018 [78] Sheen_2004 [79] Laval_2021 [76] Continuous vibration separation (CVS)…”

Section: Wireless Sensor Network (Wsn)mentioning

confidence: 99%

Section: Multiple Sourcesmentioning

confidence: 99%

Section: Appendix Amentioning

confidence: 99%

See 1 more Smart Citation

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

et al. 2022

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“…In such situations, obtaining a correlation between the signal's time and frequency contents is always advantageous. As a result, FFT analysis is ineffective for detecting bearing characteristics, which are an essential component of the vibration signature [26][27][28].…”

Section: Fast Fourier Transform (Fft)mentioning

confidence: 99%

Fault identification and remaining useful life prediction of bearings using Poincare maps, fast Fourier transform and convolutional neural networks

Mulay¹,

Majali²,

Iyengar³

et al. 2022

Math. models, eng.

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Bearings are integral components of rotating machinery and their failure tends to be a catastrophic failure of the machine. Poincare Maps are used to detect bearing failures using the concept of non-linear dynamics. Each time-domain vibration signature array has its own Poincare Map over a period of time. Fast Fourier Transform (FFT) is a method of analysing the frequency plots of a bearing signature. Convolutional Neural Networks (CNN) process the bearing Continuous Wavelet Transform images and provide the Remaining Useful Life (RUL) of the bearing. The Poincare Maps and FFT plots are used to diagnose the type and location of the fault in the bearing, whereas the CNN helps to provide the fraction of Remaining Useful Life. The study concludes that a combination of Poincare Maps, FFT analysis and Convolutional Neural Networks constitutes a robust and precise method of monitoring bearing conditions.

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“…To in-situ monitor the operating status of drivetrain bearings, the temperature, vibration and sound were measured in most of the existing monitoring technologies [2][3][4], and many studies focus on the fault diagnosis method of the bearings [5][6][7]. However, abnormal temperature, vibration and sound are the symptom of bearing failure [4], not the cause of bearing failure.…”

mentioning

confidence: 99%

Measured load spectra of the bearing in high-speed train gearbox under different gear meshing conditions

et al. 2022

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The load spectrum is a crucial factor for assessing the fatigue reliability of in-service rolling element bearings in transmission systems. For a bearing in a high-speed train gearbox, a measurement technique based on strain detection of bearing outer ring was used to instrument the bearing and determine the time histories of the distributed load in the bearing under different gear meshing conditions. Accordingly, the load spectrum of the total radial load carried by the bearing was compiled. The mean value and class interval of the obtained load spectrum were found to vary non-monotonously with the speed and torque of gear meshing, which was considered to be caused by the vibration of the shaft and the bearing cage. As the realistic service load input of bearing life assessment, the measured load spectrum under different gear meshing conditions can be used to predict gearbox bearing life realistically based on the damage-equivalent principle and actual operating conditions.

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Vibration health monitoring of rolling bearings under variable speed conditions by novel demodulation technique

Cited by 24 publications

References 41 publications

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

Fault identification and remaining useful life prediction of bearings using Poincare maps, fast Fourier transform and convolutional neural networks

Measured load spectra of the bearing in high-speed train gearbox under different gear meshing conditions

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