A summary of vibration analysis techniques for fault detection and diagnosis in bearing

Boudiaf, Adel; Djebala, Abederrazek; Bendjma, Hocine; Balaska, A.; Dahane, Amine

doi:10.1109/icmic.2016.7804187

Cited by 25 publications

(11 citation statements)

References 18 publications

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“…The bearings in the motor are connected to the rotor, which is used to minimize friction between the shafts and stationary parts. Bearing faults are mainly due to lubricant-related failures, which can be avoided by early prediction and preventive maintenance [13]. The ball bearing structure shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Detection in Induction Motors Using Line Currents

Sunder

Abishek

et al. 2021

ECTI-EEC

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This paper focuses on the development of a bearing fault detection model for induction motors using line currents. The graphical and numerical analysis of the model has been developed using the park's vector approach and envelope based on the Hilbert transform. The proposed model has been evaluated on currents measured from eight different types of induction motors. The graphical results from the Concordia pattern between d- and q-components of stator currents show that healthy bearing behaviour is circular compared to the faulty bearing's elliptical. The numerical results show that the minimum and maximum envelope of d- and q-components of stator currents is more significant than one. The sum of Kurtosis for the envelope of d- and q-components of stator currents is less than 5.0.

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Section: Introductionmentioning

confidence: 99%

Bearing Fault Detection in Induction Motors Using Line Currents

Sunder

Abishek

et al. 2021

ECTI-EEC

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“…Rolling bearing is a key part of rotary machinery. Previous studies had shown that rolling bearings were the cause of most of the faults of rotary equipment [1], and, therefore, it is of great significance to study the damage mechanism of rolling bearings. Skidding is a typical fault of high-speed light load rolling bearings [2], whose failure will induce the failure of the support shafting.…”

Section: Introductionmentioning

confidence: 99%

“…At present, achievements have been made in the research of rolling bearing vibration characteristics. Boudiaf et al [1,9] compared various fault signal analysis methods, such as FFT, short-time fast Fourier transform, envelope analysis, and empirical mode decomposition analysis to summarize the advantages and disadvantages of each diagnosis method in the study of the bearing fault analysis and identification. Li et al [10] proposed a new rolling bearing fault diagnosis method based on Adaptive Minimum Entropy Deconvolution (AMED) and Time-Delayed Feedback Monostable Stochastic Resonance (TFMSR) in the study of rolling bearing faults and verified the possibility of this method through experiments.…”

Section: Introductionmentioning

confidence: 99%

Effect of Multiple Factors on Identification and Diagnosis of Skidding Damage in Rolling Bearings under Time-Varying Slip Conditions

Chen

Xue

et al. 2019

Applied Sciences

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Skidding damage mechanism of rolling bearings is not clear, due to the influence of various coupling factors. To solve this problem, it is important to identify and diagnose skidding damage and study the vibration characteristics in rolling bearings. Based on Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT), vibration signals of rolling bearings are extracted and analyzed, and then the skidding damage of rolling bearings from multiple signals perspectives is identified. The relationship between the variation in the radial load, temperature, slip and the skidding damage of rolling bearings under time-varying slip conditions is analyzed comprehensively, and then the influence of different factors on bearing skidding damage is studied. The integrated analysis of the vibration, load, temperature, slip rate and other multivariate signals information shows the starting time of skidding damage. This research can be conducive to reduce vibration and prolong the life of rolling bearings.

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“…The effect of sensor noise is noticeable in that study. In [3], vibration analysis in monitoring rolling-element bearings was summarized and their capabilities, advantages and disadvantages were explained. In [4], the comparable efficiency of using frequency domain approach in HilbertHuang Transform for bearing fault diagnosis was handled.…”

Section: Introductionmentioning

confidence: 99%

A Signal Processing Approach for the Failure Analysis of Rolling-Element Bearing of Vehicle Brake Tester Used at a Vehicle Inspection Station

Kulac

2019

2019 IEEE East-West Design &Amp; Test Symposium (EWDTS)

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In this study, failure analysis of rolling-element bearing of vehicle brake tester used in a vehicle inspection station was considered according to the real time vibration signals. Based on vibration measurements under loads taken at intervals of about 15 days, these real time vibration signals were subjected to median filtering firstly. The total difference method proposed with this paper using autocorrelation functions' maximum and minimum values for each vibration signal was applied. Lastly, Goertzel algorithm has been used in order to obtain amplitude of specific defect frequency sample with a minimum calculation complexity for vibrational failure analysis of rolling-element bearing for the first time with this paper. It is verified with this paper that as the time passes, the values obtained by all methods above increase and excess the thresholds determined by this paper.

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A summary of vibration analysis techniques for fault detection and diagnosis in bearing

Cited by 25 publications

References 18 publications

Bearing Fault Detection in Induction Motors Using Line Currents

Bearing Fault Detection in Induction Motors Using Line Currents

Effect of Multiple Factors on Identification and Diagnosis of Skidding Damage in Rolling Bearings under Time-Varying Slip Conditions

A Signal Processing Approach for the Failure Analysis of Rolling-Element Bearing of Vehicle Brake Tester Used at a Vehicle Inspection Station

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