Proceedings ArticleDOI
Bearing fault diagnosis of a three phase induction motor using stockwell transform
Megha Singh,Abdul Gafoor Shaik +1 more
- pp 1-6
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TLDR
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.read more
Citations
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Journal ArticleDOI
Faulty bearing detection, classification and location in a three-phase induction motor based on Stockwell transform and support vector machine
Megha Singh,Abdul Gafoor Shaik +1 more
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.
Journal ArticleDOI
A Review to Diagnose Faults Related to Three-Phase Industrial Induction Motors
Muhammad Aman Sheikh,Sheikh Tahir Bakhsh,Muhammad Irfan,Nursyarizal Mohd Nor,Grzegorz Nowakowski +4 more
Proceedings ArticleDOI
Location of Defective Bearing in Three-Phase Induction Motor Using Stockwell Transform and Support Vector Machine
Megha Singh,Abdul Gafoor Shaik +1 more
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.
Book ChapterDOI
Induction Motor Internal and External Fault Detection
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.
Journal ArticleDOI
Progressive Bearing Fault Detection in a Three-Phase Induction Motor Using S-Transform via Pre-Fault Frequency Cancellation
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.
References
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Proceedings ArticleDOI
Bearing Fault Diagnosis in Induction Machine Based on Current Analysis Using High-Resolution Technique
TL;DR: In this article, a high-resolution spectral analysis of the stator current was used to detect bearing faults in an electrical induction machine, and the results showed that the proposed method yields better detection than classical spectum analysis.
Proceedings ArticleDOI
An Improvement of Stator Current Based Detection of Bearing Fault in Induction Motors
Sun Liling,Xu Boqiang +1 more
TL;DR: In this paper, an improved stator current based detection scheme for bearing fault in induction motors is proposed, which perfectly blends subdivision Fourier transform, self-adaptive filter and rotor slot harmonics based slip estimation techniques together.
Proceedings ArticleDOI
Bearing damage detection based on statistical discrimination of stator current
TL;DR: In this paper, a method is presented for discriminating stator current signals from two classes, motors in normal condition and ones with a bearing failure, based on statistical analysis of Gabor filter responses.
Proceedings ArticleDOI
Bearing damage detection via wavelet packet
Levent Eren,M.J. Devaney +1 more
TL;DR: In this paper, the stator current was analyzed via wavelet packet decomposition to detect beating defects, which enables the analysis of frequency bands that can accommodate the rotational speed dependence of the beating-defect frequencies.
Journal ArticleDOI
Detecting Bearing Faults in Line-Connected Induction Motors Using Information Theory Measures and Neural Networks
TL;DR: This work presents a predictability analysis method that provides patterns based on measures of relative entropy, Bhattacharyya distance, and Lempel–Ziv complexity estimated over reconstructed signals obtained from wavelet packet decomposition components.
Related Papers (5)
Broken Rotor Bar Fault Diagnosis of a Three-phase Induction Motor using Discrete Wavelet Transform
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Bearing damage detection via wavelet packet decomposition of the stator current
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