Continuous Wavelet Transform for Bearing Damage Detection in Electric Motors

Ayaz, Emine; Öztürk, A.; Şeker, Serhat

doi:10.1109/melcon.2006.1653299

Cited by 21 publications

(5 citation statements)

References 4 publications

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“…A Bell 7030 Tesla-meter fixed in the middle of the stator frame length to measure the tangential component of the stray flux is presented in [22]. The authors affirm that this equipment is very expensive for practical industrial applications, especially for small IMs.…”

Section: Ieee Transactions On Industrial Electronicsmentioning

confidence: 95%

Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement

Frosini

Harlişca

Szabó

2015

IEEE Trans. Ind. Electron.

230

124

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Rolling bearing faults are generally slowly progressive; therefore the development of an effective diagnostic technique could be worth to detect such faults in their incipient phase and to prevent complete failure of the motor. The methods proposed in the literature for this purpose are mainly based on measuring and analyzing the vibration and current. Here, a novel technique based on the stray flux measurement in different positions around the electrical machine is proposed. The main advantages of this method are due to the simplicity and the flexibility of the custom flux probe with its amplification and filtering stage. The flux probe can be easily positioned on the machines and adapted to a wide range of power levels. The paper also reports an extensive survey on the stray flux based fault detection methods for induction motors, prior to introducing a novel sensor/diagnostic scheme.

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Section: Ieee Transactions On Industrial Electronicsmentioning

confidence: 95%

Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement

Frosini

Harlişca

Szabó

2015

IEEE Trans. Ind. Electron.

230

124

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“…that a higher value of amplitude at higher frequency region (2 kHz to 5kHz) present all the time since the fault, use each time a ball passes the fault; the resulting impact excites the resonance in the structure. But, the diagnosis of fault is difficult by examining the frequency spectrum [10].…”

Section: E Continuous Wavelet Transform (Cwt)mentioning

confidence: 99%

Signal Processing Methods for Identification of Induction Motor Bearing Fault

Agrawal*¹,

Tiwari²,

Giri³

2019

IJRTE

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To diagnose early faults as soon as possible, the feature extraction of vibration signals is very important in real engineering applications. Recently, the advanced signal processing-based weak feature extraction method has been becoming a hot research topic. The dominant mode of failure in rolling element bearings is spalling of the races or the rolling elements. Localized defects generate a series of impact vibrations every time whenever running roller passes over the surface of a defect. Therefore, vibration analysis is a conventional method for bearing fault detection. However, the measured vibration signals of rotating machinery often present nonlinear and non-stationary characteristics. This paper deals with the diagnosis of induction motor bearing based on vibration signal analysis. It provides a comparative study between traditional signal processing methods, such as Power Spectrum, Short Time Fourier Transform, Wavelet Transform, and Hilbert Transform. Performances of these techniques are assessed on real vibration data and compared for healthy and faulty bearing.

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“…The admissibility condition ensures that Fourier transform of ψ(t) vanishes at zero frequency (Ayaz et al 2006), i.e., the average value of wavelets in time domain must be zero (Eq. 5).…”

Section: Technical Previewmentioning

confidence: 99%

Wavelet Analysis of Signals in Agriculture and Food Quality Inspection

Singh

Choudhary

Jayas

et al. 2008

Food Bioprocess Technol

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Food quality and safety have become the top priorities for agriculture and food processing industry due to the increasing consumer demand for high-quality healthy food. The food processing industry is currently focusing on using fast, precise, and nondestructive automated quality inspection techniques. Near-infrared spectroscopy, image processing, hyperspectral imaging, X-rays, and ultrasonic techniques have been researched and shown to have high potential for automated inspection. The biggest challenge in the automated inspection systems deals with signal preprocessing, denoising, feature extraction, and its re-synthesis for classification purposes. Several research studies have established that the technique of wavelet analysis can very well resolve these issues of signal processing in many systems used for quality inspection of agricultural and food products. The objective of this paper is to discuss the theory of wavelet analysis and review its application in signal processing and feature extraction for quality monitoring of agricultural and food products.

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Continuous Wavelet Transform for Bearing Damage Detection in Electric Motors

Cited by 21 publications

References 4 publications

Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement

Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement

Signal Processing Methods for Identification of Induction Motor Bearing Fault

Wavelet Analysis of Signals in Agriculture and Food Quality Inspection

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