Optimal arcing fault detection using signal processing techniques

Signal processing is considered as an efficient technique to detect the faults in three-phase induction motors. Detection of different varieties of faults in the rotor of the motor are widely studied at the industrial level. To extend further, this research article presents the analysis on various signal processing techniques for fault detection in three-phase induction motor due to the damages in rotor bar. Usually, Fast Fourier Transform (FFT) and STFT are used to analyze the healthy and faulty motor conditions based on the signal characteristics. The proposed study covers the advantages and limitations of the proposed wavelet transform (WT) and each technique for detecting the broken bar of induction motors. The good frequency information can be collected from FFT techniques for handling multiple faults identification in three-phase induction motor. Despite the hype, the detection accuracy gets reduced during the dynamic condition of the machine because the frequency information on sudden time changes cannot be employed by FFT. The WT method signal analysis is compared with FFT to propose fault detection method for induction motor. The WT method is proving better accuracy when compared to all existing methods for signal information analysis. The proposed research work has simulated the proposed method with MATLAB / SIMULINK and it helps to effectively detect the healthy and faulty conditions of the motor.

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Advanced Signal Processing Techniques for Bearing Fault Detection in Induction Motors

2018 15th International Multi-Conference on Systems, Signals & Devices (SSD) ◽

10.1109/ssd.2018.8570403 ◽

2018 ◽

Cited By ~ 2

Author(s):

Firas Ben Abid ◽

Ahmed Braham

Keyword(s):

Signal Processing ◽

Fault Detection ◽

Induction Motors ◽

Bearing Fault ◽

Bearing Fault Detection ◽

Processing Techniques ◽

Signal Processing Techniques

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Local Sensor Validation

Measurement and Control ◽

10.1177/002029408902200502 ◽

1989 ◽

Vol 22 (5) ◽

pp. 132-141 ◽

Cited By ~ 39

Author(s):

S K Yung ◽

D W Clarke

Keyword(s):

Time Series ◽

Signal Processing ◽

Fault Detection ◽

Process Model ◽

Time Series Models ◽

Standard Time ◽

Sensor Validation ◽

Processing Techniques ◽

Signal Processing Techniques ◽

Hierarchical Scheme

Current approaches to plant fault detection require an overall process model. This paper argues that a hierarchical scheme is more efficient in which the lowest level concentrates on validating the signals transmitted by individual sensors. Signals are described in terms of standard time-series models. A fault is defined by the signal's deviation from its expected behaviour and various signal processing techniques are described which detect these aberrations.

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Recent Advances in Vibration Signal Processing Techniques for Gear Fault Detection-A Review

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.430.78 ◽

2013 ◽

Vol 430 ◽

pp. 78-83 ◽

Cited By ~ 1

Author(s):

Rusmir Bajrić ◽

Ninoslav Zuber ◽

Safet Isić

Keyword(s):

Signal Processing ◽

Fault Detection ◽

Vibration Signal ◽

Analysis Time ◽

Time Frequency ◽

Detection Techniques ◽

Advantages And Disadvantages ◽

Gear Fault ◽

Processing Techniques ◽

Signal Processing Techniques

This paper provides a review of the literature, progress and changes over the years on fault detection of gears using vibration signal processing techniques. Analysis of vibration signals generated by gear in mesh has shown its usefulness in industrial gearbox condition monitoring. Vibration measurement provides a very efficient way of monitoring the dynamic conditions of a machine such as gearbox. Various vibration analysis methods have been proposed and applied to gear fault detection. Most of the traditional signal analysis techniques are based on the stationary assumption. Such techniques can only provide analyses in terms of the statistical average in the time or frequency domain, but can not reveal the local features in both time and frequency domains simultaneously. Frequency/quefrency analysis, time/statistical analysis, time-frequency analysis and cyclostationarity analysis are reviewed in regard for stationary and nonstationary operation. The use of vibration signal processing detection techniques is classified and discussed. The capability of each technique, fundamental principles, advantages and disadvantages and practical application for gear faults detection have been examined by literature review.

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