Advanced detection of rolling bearing spalling from de-noising vibratory signals

Bolaers, Fabrice; Cousinard, Olivier; Marconnet, Patrick; Rasolofondraibe, Lanto

doi:10.1016/s0967-0661(03)00048-0

Cited by 24 publications

(10 citation statements)

References 7 publications

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“…The simplest method is to use overall root-mean-square (RMS) level and crest factor, i.e., the ratio of peak value to RMS. This method has been applied with limited success in the detection of localized defects [28][29][30]. Probability density has also been used popularly for bearing defect detection [8,9,29,31,32].…”

Section: Time Domain Methodsmentioning

confidence: 99%

Condition Monitoring of Low Speed Bearings: A Comparative Study of the Ultrasound Technique Versus Vibration Measurements

Kim

Tan

Mathew

et al.

Engineering Asset Management

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Bearing failure is often attributed to be one of the major causes of breakdown in industrial rotating machines that operate at high and low speeds. This paper presents results of a comparative experimental study on the application of the ultrasound technique for condition monitoring of low speed rolling element bearings and conventional vibration measurements with seeded faults on inner-race defects. The effectiveness of the ultrasound technique is demonstrated through signal processing techniques; use of statistical parameters derived from the time domain, and enveloped spectra in the frequency domain. The results reveal that the ultrasound technique is more effective in detecting low speed bearings failure than that of the vibration measurement.

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Section: Time Domain Methodsmentioning

confidence: 99%

Condition Monitoring of Low Speed Bearings: A Comparative Study of the Ultrasound Technique Versus Vibration Measurements

Kim

Tan

Mathew

et al.

Engineering Asset Management

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“…For a discrete signal x of length N and mean x, three major scalar indicators are thus defined: the Crest factor (3), the K factor (4) and the Kurtosis (5) [14] Crest…”

Section: Scalar Indicators For the Detection Of Bearing Faultsmentioning

confidence: 99%

Comparison between vibration and stator current analysis for the detection of bearing faults in asynchronous drives

Trajin

Regnier

Faucher

2010

IET Electr. Power Appl.

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“…Generally, these techniques can be divided into non-parametric and parametric. When using a non-parametric technique, signals can be analysed in the time domain, using parameters such as kurtosis and crest factors [10][11][12], in the frequency domain, e.g. through application of the fast Fourier transform (FFT) [13], and/or in the timefrequency domain, using techniques such as the wavelet transform [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling

Al-Bugharbee

Trendafilova

2016

Journal of Sound and Vibration

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This study proposes a methodology for rolling element bearings fault diagnosis which gives a complete and highly accurate identification of the faults present. It has two main stages: signals pretreatment, which is based on several signal analysis procedures, and diagnosis, which uses a pattern-recognition process. The first stage is principally based on linear time invariant autoregressive modelling. One of the main contributions of this investigation is the development of a pretreatment signal analysis procedure which subjects the signal to noise cleaning by singular spectrum analysis and then stationarisation by differencing. So the signal is transformed to bring it close to a stationary one, rather than complicating the model to bring it closer to the signal. This type of pre-treatment allows the use of a linear time invariant auto-regressive model and improves its performance when the original signals are non-stationary. This contribution is at the heart of the proposed method, and the high accuracy of the diagnosis is a result of this procedure. The methodology emphasizes the importance of preliminary noise cleaning and stationarisation. And it demonstrates that the information needed for fault identification is contained in the stationary part of the measured signal. The methodology is further validated using three different experimental setups, demonstrating very high accuracy for all of the applications. It is able to correctly classify nearly 100 percent of the faults with regard to their type and size. This high accuracy is the other important contribution of this methodology. Thus, this research suggests a highly accurate methodology for rolling element bearing fault diagnosis which is based on relatively simple procedures. This is also an advantage, as the simplicity of the individual processes ensures easy application and the possibility for automation of the entire process

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Advanced detection of rolling bearing spalling from de-noising vibratory signals

Cited by 24 publications

References 7 publications

Condition Monitoring of Low Speed Bearings: A Comparative Study of the Ultrasound Technique Versus Vibration Measurements

Condition Monitoring of Low Speed Bearings: A Comparative Study of the Ultrasound Technique Versus Vibration Measurements

Comparison between vibration and stator current analysis for the detection of bearing faults in asynchronous drives

A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling

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