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Proceedings ArticleDOI

Bearing fault diagnosis of a three phase induction motor using stockwell transform

TL;DR: In this paper, the S-transform based feature extraction can be effectively utilized for bearing fault detection and diagnosis in a three phase induction motor using Stockwell transform of stator currents.
Abstract: This paper presents bearing fault detection and its diagnosis in a three phase induction motor using Stockwell transform of stator currents. The maximum magnitude and maximum phase angle plots are obtained from S-transform for various bearing conditions both on shaft-side and fan-side. The standard deviation of these plots are utilized to detect and analyze the bearing faults. The various bearing faults analyzed under case studies include defects in innerrace, outerrace, cage and balls. The experimental study shows that the S-transform based feature extraction can be effectively utilized for bearing fault detection and diagnosis in three phase induction motor.
Citations
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Journal ArticleDOI
TL;DR: F faulty bearing detection, classification and its location in a three-phase induction motor using Stockwell transform and Support vector machine is presented.

55 citations

Proceedings ArticleDOI
01 Jun 2018
TL;DR: This paper presents a technique to locate defective bearing based on Stockwell Transform of stator current signals and has been tested successfully for the bearing faults such as ball and outerrace fault.
Abstract: The rotor shaft of a three-phase induction motor is suspended on two bearings; one on fan-side, other on load-side. This paper presents a technique to locate defective bearing based on Stockwell Transform of stator current signals. The statistical properties of Stockwell transform of current signals are used to form feature vector. Principal Component Analysis is used to extract components with high variability from this feature vector. The top principal components thus extracted, are utilized to locate the defective bearing with the help of Support Vector Machine. The proposed algorithm has been tested successfully for the bearing faults such as ball and outerrace fault.

Cites methods from "Bearing fault diagnosis of a three ..."

  • ...Defective bearings in a three-phase induction motor can be effectively located by analyzing the stator currents with the help of Stockwell Transform....

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  • ...Stockwell Transform (ST) [21] is a phase-corrected version of Continuous Wavelet Transform (CWT) which uses Gaussian window as its mother wavelet....

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  • ...The stator current signals are analyzed with Stockwell Transform (ST) which results in a ST matrix....

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  • ...Among these statistical features, standard deviation (SD) is reported to be useful parameter in bearing fault diagnosis based on Stockwell Transform [14]....

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  • ...In this paper, an effort has been made to locate the defective bearing using Support Vector Machine (SVM) which is fed with principal components obtained from Stockwell Transform of stator current signals....

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Book ChapterDOI
01 Jan 2019
TL;DR: S-Transformation, which is superior as compared to CWT and STFT as it does not contain any cross terms, is used for bearing fault detection, and random forest, an algorithm which is easy to implement and requires minimum memory, are used for detection of external faults.
Abstract: Induction motors are extensively used motor type for various industrial applications for the reason that they are robust, simple in structure, and efficient. On the other hand, induction motors are prone to different faults during their lifetime due to hostile environments. If the fault is not detected in its rudimentary phase, it may cause unexpected shut down of the entire system and colossal loss in industry. It is conspicuous that scope of this field is huge. This work presents detection of internal and external faults of induction motor. S-Transformation, which is superior as compared to CWT and STFT as it does not contain any cross terms, is used for bearing fault detection, and random forest, an algorithm which is easy to implement and requires minimum memory, is used for detection of external faults. The fault can be detected with more accuracy in premature state leads to improve the reliability of the system.
Journal ArticleDOI
TL;DR: In this paper , the authors proposed spectral analysis of stator current to estimate motor faults, FFT analysis is commonly preferred, but the problems associated with normal FFT analyses will mislead the fault diagnosis, and each technique requires special attention to get good results.
Abstract: Detection of bearing faults have become crucial in electrical machines, particularly in induction motors. Conventional monitoring procedures using vibration sensors, temperature sensors, etc. are costly and need more tests to estimate the nature of fault. Hence, the current monitoring attracts the concentration of many industries for continuous monitoring. Spectral analysis of stator current to estimate motor faults, FFT analysis, is commonly preferred. But the problems associated with normal FFT analysis will mislead the fault diagnosis. Therefore, advanced spectral methods like wavelet transforms, matrix pencil method, MUSIC algorithm, s-transforms have been proposed. But each technique requires special attention to get good results. On the other hand, faults experienced by the induction motor can be categorized into bearing-related, rotor- and stator-related, and eccentricity. Among these faults, bearing damage accounts for 40-90% and requires additional concentration to estimate.
References
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Journal ArticleDOI
TL;DR: The S transform is shown to have some desirable characteristics that are absent in the continuous wavelet transform, and provides frequency-dependent resolution while maintaining a direct relationship with the Fourier spectrum.
Abstract: The S transform, which is introduced in the present correspondence, is an extension of the ideas of the continuous wavelet transform (CWT) and is based on a moving and scalable localizing Gaussian window. It is shown to have some desirable characteristics that are absent in the continuous wavelet transform. The S transform is unique in that it provides frequency-dependent resolution while maintaining a direct relationship with the Fourier spectrum. These advantages of the S transform are due to the fact that the modulating sinusoids are fixed with respect to the time axis, whereas the localizing scalable Gaussian window dilates and translates.

2,752 citations


"Bearing fault diagnosis of a three ..." refers background in this paper

  • ...al [22] proposed S-transform that can be considered as an extension to the continuous wavelet transform with phase correction....

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Journal Article
TL;DR: The S transform as discussed by the authors is an extension to the ideas of the Gabor transform and the Wavelet transform, based on a moving and scalable localising Gaussian window and is shown here to have characteristics that are superior to either of the transforms.
Abstract: The S transform, an extension to the ideas of the Gabor transform and the Wavelet transform, is based on a moving and scalable localising Gaussian window and is shown here to have characteristics that are superior to either of the transforms. The S transform is fully convertible both forward and inverse from the time domain to the 2-D frequency translation (time) domain and to the familiar Fourier frequency domain. Parallel to the translation (time) axis, the S transform collapses as the Fourier transform. The amplitude frequency-time spectrum and the phase frequency-time spectrum are both useful in defining local spectral characteristics. The superior properties of the S transform are due to the fact that the modulating sinusoids are fixed with respect to the time axis while the localising scalable Gaussian window dilates and translates. As a result, the phase spectrum is absolute in the sense that it is always referred to the origin of the time axis, the fixed reference point. The real and imaginary spectrum can be localised independently with a resolution in time corresponding to the period of the basis functions in question. Changes in the absolute phase ofa constituent frequency can be followed along the time axis and useful information can be extracted. An analysis of a sum of two oppositely progressing chirp signals provides a spectacular example of the power of the S transform. Other examples of the applications of the Stransform to synthetic as well as real data are provided.

2,323 citations

Proceedings ArticleDOI
02 Oct 1994
TL;DR: In this article, the authors used motor current spectral analysis to detect rolling-element bearing damage in induction machines, where the bearing failure modes were reviewed and bearing frequencies associated with the physical construction of the bearings were defined.
Abstract: This paper addresses the application of motor current spectral analysis for the detection of rolling-element bearing damage in induction machines. Vibration monitoring of mechanical bearing frequencies is currently used to detect the presence of a fault condition. Since these mechanical vibrations are associated with variations in the physical air gap of the machine, the air gap flux density is modulated and stator currents are generated at predictable frequencies related to the electrical supply and vibrational frequencies. This paper takes the initial step of investigating the efficacy of current monitoring for bearing fault detection by correlating the relationship between vibration and current frequencies caused by incipient bearing failures. The bearing failure modes are reviewed and the characteristic bearing frequencies associated with the physical construction of the bearings are defined. The effects on the stator current spectrum are described and the related frequencies determined. This is an important result in the formulation of a fault detection scheme that monitors the stator currents. Experimental results which show the vibration and current spectra of an induction machine with different bearing faults are used to verify the relationship between the vibrational and current frequencies. The test results clearly illustrate that the stator current signature can be used to identify the presence of a bearing fault. >

703 citations

Journal ArticleDOI
TL;DR: New models for the influence of rolling-element bearing faults on induction motor stator current are described, based on two effects of a bearing fault: the introduction of a particular radial rotor movement and load torque variations caused by the bearing fault.
Abstract: This paper describes a new analytical model for the influence of rolling-element bearing faults on induction motor stator current. Bearing problems are one major cause for drive failures. Their detection is possible by vibration monitoring of characteristic bearing frequencies. As it is possible to detect other machine faults by monitoring the stator current, a great interest exists in applying the same method for bearing fault detection. After a presentation of the existing fault model, a new detailed approach is proposed. It is based on the following two effects of a bearing fault: 1. the introduction of a particular radial rotor movement and 2. load torque variations caused by the bearing fault. The theoretical study results in new expressions for the stator current frequency content. Experimental tests with artificial and realistic bearing damage were conducted by measuring vibration, torque, and stator current. The obtained results by spectral analysis of the measured quantities validate the proposed theoretical approach.

455 citations

Journal ArticleDOI
TL;DR: In this paper, bearing defect is detected using the stator current analysis via Meyer wavelet in the wavelet packet structure, with energy comparison as the fault index, and the presented method is evaluated using experimental signals.

181 citations


"Bearing fault diagnosis of a three ..." refers methods in this paper

  • ...[9] proposed a method based on WPD of stator currents to detect bearing faults using Meyer wavelet as mother wave....

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