Cyclostationarity of Acoustic Emissions (AE) for monitoring bearing defects

Kilundu, Bovic; Chiementin, Xavier; Duez, J.; Mba, David

doi:10.1016/j.ymssp.2011.01.020

Cited by 73 publications

(47 citation statements)

References 20 publications

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“…Antony has suggested that these slips are non-stationary process and may be approximated as quasi-cyclostationary over only a limited period of time [15]. Kilundu et al [13] note that the acoustic emission is similar to vibrations and suggest the same cyclostationarity properties for AE signal arising from a defective bearing. An efficient method for testing cyclostationarity of a signal X(t) (residual part after extracting the deterministic part of the signal) is to compute the two-dimensional Fourier transform of its autocorrelation function R xx (t, τ ) i.e…”

Section: Cyclostationaritymentioning

confidence: 99%

“…Liao et al [11] used wavelet analysis and Zvokelj et al [12] were interested in the application of empirical mode decomposition (EMD) applied to AE signals. Recently, Kilundu et al [13] applied the cyclostationary on acoustic emission and showed the effectiveness of the spectral correlation and the ICS (integrated correlation spectral) indicator for monitoring bearing defect but with relatively big defect sizes.…”

Section: Introductionmentioning

confidence: 99%

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Cyclostationarity applied to acoustic emission and development of a new indicator for monitoring bearing defects

Kedadouche

Thomas

Tahan

2014

Mechanics & Industry

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-The exploitation of cyclostationarity properties of vibratory signals is now more widely used for monitoring rotating machinery and especially for diagnosing bearing defects. The acoustic emission (AE) technology has also emerged as a reliable tool for preventive maintenance of rotating machines. In this study, we propose an experimental study that characterizes the cyclostationary aspect of acoustic emission (AE) signals recorded from a defective bearing (40 µm on the outer race) to see its efficiency to detect a defect at its very early stage of degradation. An industrial sensor (UE10 000) is used. An electrical circuit converts the high frequency signal into an audible signal by heterodyning. The cyclic spectral density, which is a tool dedicated that put into evidence the presence of cyclostationarity, is used for characterizing the cyclostationary. Two new indicators based on this cyclostationary technique are proposed and compared for early detection of defective bearings.

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Section: Cyclostationaritymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cyclostationarity applied to acoustic emission and development of a new indicator for monitoring bearing defects

Kedadouche

Thomas

Tahan

2014

Mechanics & Industry

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“…The test results revealed a linear relationship between AE energy and fracture area or depth [4]. B. Kilundu employed a cyclostationary technique and proposed an indicator that was more sensitive to the continuous monitoring of defects compared with traditional temporal indicators (e.g., RMS, kurtosis, crest factor) [5].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic Entropy

Long

Liu

2015

Entropy

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Abstract:The randomness and fuzziness that exist in rolling bearings when faults occur result in uncertainty in acquisition signals and reduce the accuracy of signal feature extraction. To solve this problem, this study proposes a new method in which cloud model characteristic entropy (CMCE) is set as the signal characteristic eigenvalue. This approach can overcome the disadvantages of traditional entropy complexity in parameter selection when solving uncertainty problems. First, the acoustic emission signals under normal and damage rolling bearing states collected from the experiments are decomposed via ensemble empirical mode decomposition. The mutual information method is then used to select the sensitive intrinsic mode functions that can reflect signal characteristics to reconstruct the signal and eliminate noise interference. Subsequently, CMCE is set as the eigenvalue of the reconstructed signal. Finally, through the comparison of experiments between sample entropy, root mean square and CMCE, the results show that CMCE can better represent the characteristic information of the fault signal.

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“…This suggests that the cyclostationary properties of AE signals from a defective bearing are the same as vibration signals. There are very few studies studying acoustic emission and cyclostationarity for bearing defects [57]. The most relevant study was that conducted by Kilundu et al [57].…”

Section: Acoustic Emissionmentioning

confidence: 99%

“…There are very few studies studying acoustic emission and cyclostationarity for bearing defects [57]. The most relevant study was that conducted by Kilundu et al [57]. The study was conducted under constant speed of 1500 rpm with seeded bearing defects, comparing cyclic spectral correlation and envelope spectrum when they are applied to AE analysis.…”

Section: Acoustic Emissionmentioning

confidence: 99%

Multi-Sensor Condition Monitoring of Bearings Using Support Vector Machines

Kan¹

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Keywords AbstractBearings are one of the most commonly-used and crucial components in the majority of rotating machines in industrial applications. Failure of this component is common and can lead to machine breakdown. If it is not detected in time, bearing faults can even lead to catastrophic failures. An effective diagnostic system provides early warning and fault identification such that any machine breakdown can be avoided. Condition monitoring can benefit plant operators resulting in shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning.However, machines and associated systems today are increasingly complex, while diagnostic techniques have been mainly applied to relatively simple rotating machines. Therefore, there is a need for research on condition monitoring techniques that can be applied to complex systems in variable operating conditions. This thesis presents a study on bearing condition monitoring under variable operating condition using Support Vector Machines. Data collected from multiple sensors including accelerometers, acoustic emission sensors and tachometers are used for the studies presented in this thesis. Support Vector Machines are an efficient method for classification and prediction, and shows outstanding performance in modelling and generalization.The study begins with collecting bearing condition monitoring data. Vibration analysis is a well-established technique, while acoustic emission is gaining an increasing amount of attention especially on its capability to detect bearing incipient fault such as subsurface cracks. Existing studies so far have not demonstrated AE's diagnostic capabilities using naturally-damaged bearing, and only a very few studies adopt envelope and cyclostationary analysis on bearing fault diagnosis with this sensor technology. Due to the second-order cyclostationary nature of bearing signals and the success of envelope analysis in analysing bearing signals, the diagnostic and prognostic studies presented in this thesis make use of both envelope analysis and cyclostationary analysis to analyse vibration and acoustic emission bearing signals that will be collected from bearing diagnostics and run-to-failure prognostic experiments through a bearing diagnostic test-rig and a bearing prognostic test-rig.Using the collected data, the effectiveness and diagnostic capability of cyclostationary analysis will first be assessed using feature analysis with support vector machine. A diagnostic study is then conducted, investigating the diagnostic effectiveness of acoustic emission and its superiority to vibration, especially in the detection of bearing subsurface cracks. Through comparison of AE with vibration signals, this study confirms AE's superiority in detecting subsurface cracks in bearings and the absence of vibration symptoms of subsurface cracks.This diagnostic study has also shown the crucial ability of the cyclic logarithmic envelope iii spectrum in separating different cyc...

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Cyclostationarity of Acoustic Emissions (AE) for monitoring bearing defects

Cited by 73 publications

References 20 publications

Cyclostationarity applied to acoustic emission and development of a new indicator for monitoring bearing defects

Cyclostationarity applied to acoustic emission and development of a new indicator for monitoring bearing defects

Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic Entropy

Multi-Sensor Condition Monitoring of Bearings Using Support Vector Machines

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