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

Kedadouche, Mourad; Thomas, Marc; Tahan, Antoine

doi:10.1051/meca/2014061

Cited by 7 publications

(10 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A signal is said to be cyclostationary at an order 'p' if it exhibits periodic statistical characteristics at the order 'p' [11]. Let x[n], n0, 1,…M1 be the signal of interest captured at a sampling frequency Fs.…”

Section: A Cyclic Spectrum Analysismentioning

confidence: 99%

Classification of Multiple Electromagnetic Interference Events in High-Voltage Power Plant

Mitiche

Morison

Nesbitt

et al. 2018

2018 53rd International Universities Power Engineering Conference (UPEC)

View full text Add to dashboard Cite

This paper addresses condition assessment of electrical assets contained in High-Voltage (HV) electrical power plants. Our work introduces a novel analysis approach of multiple event signals related to faults, and which are measured using Electro-Magnetic Interference method. The proposed method transfers the expert's knowledge on events presence in the signals to an intelligent system which could potentially be used for automatic EMI diagnosis. Cyclic spectrum analysis is used as feature extraction to efficiently extract the repetitive rate and the dynamic discharge level of the events, and multi-class support vector machine is adopted for their classification. This first and novel method achieved successful results which may have potential implications on developing a framework for automatic diagnosis tool of EMI events.

show abstract

Section: A Cyclic Spectrum Analysismentioning

confidence: 99%

Classification of Multiple Electromagnetic Interference Events in High-Voltage Power Plant

Mitiche

Morison

Nesbitt

et al. 2018

2018 53rd International Universities Power Engineering Conference (UPEC)

View full text Add to dashboard Cite

show abstract

“…Thus, defect detection in rolling elements bearings has been the subject of extensive researches. Different experimental methods have been proposed for detection and diagnosis of bearings which may be broadly classified as vibration and acoustic measurements [1], temperature measurements, defect signatures in the stator current of motors, wear debris and lubricant analysis. More recent studies on induction motors concentrate on monitoring electrical signals such as stator current, because the vibration produced by defects is also modulated on the stator current.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault detection using motor current signal analysis based on wavelet packet decomposition and Hilbert envelope

Imaouchen

Alkama

Thomas

2015

MATEC Web of Conferences

View full text Add to dashboard Cite

Abstract. To detect rolling element bearing defects, many researches have been focused on Motor Current Signal Analysis (MCSA) using spectral analysis and wavelet transform. This paper presents a new approach for rolling element bearings diagnosis without slip estimation, based on the wavelet packet decomposition (WPD) and the Hilbert transform. Specifically, the Hilbert transform first extracts the envelope of the motor current signal, which contains bearings fault-related frequency information. Subsequently, the envelope signal is adaptively decomposed into a number of frequency bands by the WPD algorithm. Two criteria based on the energy and correlation analyses have been investigated to automate the frequency band selection. Experimental studies have confirmed that the proposed approach is effective in diagnosing rolling element bearing faults for improved induction motor condition monitoring and damage assessment.

show abstract

“…Ref. [1][2][3][4][5][6][7][8][9][10]. However, all these studies are conducted based on bearing seeded defect or through modification to the bearing components.…”

Section: Research Gapmentioning

confidence: 99%

“…As a consequence of this different physical domain, accelerometers operate at much lower frequencies than AE sensors, typically up to [10][11][12][13][14][15][16][17][18][19][20] kHz instead of hundreds of kHz, corresponding to larger wavelengths and therefore involving motion of the structure as a whole. The low frequency vibrations from small race defects generate negligible energy to the system compared with the surrounding noise, thus hindering early detection of faults through accelerometers.…”

Section: Acoustic Emissionmentioning

confidence: 99%

“…its fault detection capability once the bearing degrades to crack propagation [1][2][3][4][5][6][7][8][9][10]. All experimental studies conducted so far, except those by Mba and his team at Cranfield University [ [1][2][3][4][5][6]53], have focused on seeded surface spalls and scratches, and despite suggesting a higher sensitivity of AE sensors, they have not investigated the possibility of detecting subsurface cracks.…”

Section: Acoustic Emissionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Sensor Condition Monitoring of Bearings Using Support Vector Machines

Kan¹

View full text Add to dashboard Cite

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...

show abstract

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

Cited by 7 publications

References 19 publications

Classification of Multiple Electromagnetic Interference Events in High-Voltage Power Plant

Classification of Multiple Electromagnetic Interference Events in High-Voltage Power Plant

Bearing fault detection using motor current signal analysis based on wavelet packet decomposition and Hilbert envelope

Multi-Sensor Condition Monitoring of Bearings Using Support Vector Machines

Contact Info

Product

Resources

About