Feature extraction related to bearing damage in electric motors by wavelet analysis

Şeker, Serhat; Ayaz, Emine

doi:10.1016/s0016-0032(03)00015-2

Cited by 78 publications

(46 citation statements)

References 8 publications

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“…It has been used for numerous studies in fault diagnostics such as engines [16], bearings [17] [18], gears [19] [20] [21], and also has been applied to the feature extraction in acoustic emission study.…”

Section: A Wavelet Transformmentioning

confidence: 99%

Characterisation of acoustic emissions for the frictional effect in engines using wavelets based multi-resolution analysis

Wei

Wang

et al. 2015

2015 21st International Conference on Automation and Computing (ICAC)

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Abstract-The friction between piston ring-cylinder liner is a major cause of energy losses in internal combustion engines. However, no experimental method is available to measure and analyze the fictional behavior. This paper focuses on the investigation of using acoustic emission (AE) to characterize the friction online. To separate the effect relating to friction sources, wavelets multi-resolution analysis is used to suppress interfering AE events due to valve impacts and combustion progress. Then a wavelet envelope indicator is developed to highlight AE contents from friction induced AE contents. The results show that the AE contents in the middle strokes correlate closely with viscous friction process as their amplitudes exhibit a continuous profile similar to piston speed. Furthermore, the AE envelope indicator proposed can distinguish the differences between two types of lubrication oils, showing superior performance of AE based online lubrication diagnosis.

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Section: A Wavelet Transformmentioning

confidence: 99%

Characterisation of acoustic emissions for the frictional effect in engines using wavelets based multi-resolution analysis

Wei

Wang

et al. 2015

2015 21st International Conference on Automation and Computing (ICAC)

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“…A lot of analysis techniques and method has been reported in the field of condition monitoring and fault diagnosis in which the raw vibration signal collected from the motor after conditioning and DC component removal is used in fault detection [5][13]. The time synchronous averaging (TSA) is a preprocessing technique in which vibration signal coming out of sensor, is segmented based on the synchronous (Trigger) signal.…”

Section: Fault Detection and Parameter Extractionmentioning

confidence: 99%

“…Bechhoefer E & Michel kingley (2009) presented the performance evaluation method of the time synchronous averaging when no tachometer signal is available. They also proposed the background and properties of the TSA [4].A RMS feature extraction related to the bearing damage using wavelet transform for the different aging cycle of the machine is proposed by Seker S. et,al (2003) [5].Wang H. et al (2009) developed a method based on the kurtosis wave and information divergence for rolling element bearing fault diagnosis, also compare the proposed method with the traditional envelop spectra [6].…”

Section: Iintroductionmentioning

confidence: 99%

Computation Effort and Effect of Signal Processing Techniques in Condition Monitoring and Fault Diagnosis of Machine

Kumar¹,

Mishra²

2015

IJAREEIE

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Vibration signal analysis is robust method of incipient fault diagnosis and condition monitoring. Vibration signature carries dynamics information of machine, when the fault developed in the machine some of the dynamics of machine gets change which changes the vibration pattern. The machine fault diagnosis based on continues assessment procedure need effective signal processing and feature extraction technique. A computation effort of signal processing technique plays important role in the real time condition monitoring and fault diagnosis thus an effort is given for finding and verifying the effectiveness of the different signal processing techniques in terms of the computation effort. The robustness of wavelet transform is also discussed in this reseach. The experimental outcomes show that the proposed method can used as effective tool of condition monitoring.

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“…(1)(2)(3)(4)(5)(6)(7) This paper addresses fault detection methods in induction motors together with theory and applications on experimental data acquired during performance test of the motors subjected to accelerated aging (6)(7) . Detection of eccentricity fault (7)(8)(9) , bearing fault (10)(11)(12)(13)(14)(15)(16)(17)(18) , and stator insulation fault (19)(20) is considered. Applications of statistical methods, power spectral density analysis, coherence analysis, continuous and discrete wavelet transform, autoregressive modeling method, adaptive neuro-fuzzy inference system, artificial neural network is presented by means of the experimental data.…”

Section: Introductionmentioning

confidence: 99%

A Review Study on Mathematical Methods for Fault Detection Problems in Induction Motors

Ayaz¹

2014

Balkan Journal of Electrical and Computer Engineering

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Induction motors are frequently used in industrial processes. Failure of these machines may cause economic, quality and safety losses. In this paper, the mathematical methods used in detection of mechanical and electrical faults of these motors are reviewed together with theory and application examples on the current and vibration data which is acquired during performance tests of the motors followed by accelerated aging.

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Feature extraction related to bearing damage in electric motors by wavelet analysis

Cited by 78 publications

References 8 publications

Characterisation of acoustic emissions for the frictional effect in engines using wavelets based multi-resolution analysis

Characterisation of acoustic emissions for the frictional effect in engines using wavelets based multi-resolution analysis

Computation Effort and Effect of Signal Processing Techniques in Condition Monitoring and Fault Diagnosis of Machine

A Review Study on Mathematical Methods for Fault Detection Problems in Induction Motors

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