scholarly journals Fault Detection of Stator Inter-Turn Short-Circuit in PMSM on Stator Current and Vibration Signal

2018 ◽  
Vol 8 (9) ◽  
pp. 1677 ◽  
Author(s):  
Hong Liang ◽  
Yong Chen ◽  
Siyuan Liang ◽  
Chengdong Wang
Keyword(s):  
Fault Detection ◽  
Wavelet Packet ◽  
Short Circuit ◽  
Permanent Magnet Synchronous Motor ◽  
Vibration Signal ◽  
Frequency Method ◽  
Time Frequency ◽  
Stator Current ◽  
Three Phase ◽  
Short Circuit Fault

The stator inter-turn short circuit fault is one of the most common and key faults in permanent magnet synchronous motor (PMSM). This paper introduces a time–frequency method for inter-turn fault detection in stator winding of PMSM using improved wavelet packet transform. Both stator current signal and vibration signal are used for the detection of short circuit faults. Two different experimental data from a three-phase PMSM were processed and analyzed by this time–frequency method in LabVIEW. The feasibility of this approach is shown by the experimental test.

New Approach for the Indirect Detection of Blade-to-Stator Rubbing in Turbo-Machinery Using Wavelet Techniques

2014 ◽  
Author(s):  
Fadi Al-Badour ◽  
Lahouari Cheded ◽  
M. Sunar
Keyword(s):  
Fault Detection ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Indirect Detection ◽  
Blade Vibration ◽  
Time Frequency ◽  
Mechanical Faults ◽  
Shaft Vibration ◽  
Turbo Machinery ◽  
Wavelet Techniques

This paper introduces an efficient and powerful approach to fault detection in rotating machinery using time-frequency analysis based on the wavelet transform of the monitored shaft vibration signal. Wavelet techniques are one of the latest powerful tools in analyzing the transient information for condition monitoring and fault detection using vibration signature. The proposed technique combines both the Continuous Wavelet and the Wavelet Packet Transforms. In particular, it exploits the use of the modulus of the local maxima lines in the wavelet domain, to detect impulsive mechanical faults through shaft vibration such as impact blade-to-stator rubbing in turbo machinery. The proposed new wavelet-based signal processing method was able to detect the singularity in the measured shaft vibration, which was generated by blade rubbing. The singularity detection achieved by the new method was very well supported by its counterpart based on the direct blade vibration measurements. Our proposed technique was favorably compared with both the time wave and the traditional Fourier Transform techniques. In fact, both the analysis and the extensive simulation work show the superiority of the combined approach (Wavelet Packet Transform and Maxima Lines) over the traditional Fourier-based method, in reliably diagnosing impulsive mechanical faults.

Diagnostics of Stator Winding Faults in a Three-Phase Asynchronous Motor Using Park’s Vector Analysis

Vestnik MEI ◽  
2021 ◽  
pp. 69-74
Author(s):  
Muhammad Deeb ◽  
◽  
Gassan Ibragim ◽  
Talal Assaf ◽  
◽  
...  
Keyword(s):  
Induction Motor ◽  
Short Circuit ◽  
Asynchronous Motor ◽  
Vector Model ◽  
Stator Winding ◽  
Stator Windings ◽  
Stator Current ◽  
Experimental Part ◽  
Three Phase ◽  
Short Circuit Fault

The study addresses the problem of detecting a short circuit fault in the three-phase induction motor winding by monitoring the stator current Park vector (Lissajous curves). Park's vector model is implemented using the Matlab software package. The experimental part of the study was carried out on an 11 kW three-phase induction motor. The Lissajous curves obtained for a healthy motor and a motor with short-circuited turns under various load conditions were compared with each other. The obtained results have demonstrated the effectiveness of the proposed method for detecting interturn short circuit faults in the three-phase stator windings of induction motors.

scholarly journals Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Gayatridevi Rajamany ◽  
Sekar Srinivasan ◽  
Krishnan Rajamany ◽  
Ramesh K. Natarajan
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Induction Motor ◽  
Short Circuit ◽  
Positive Sequence ◽  
Negative Sequence ◽  
Three Phase ◽  
Sequence Components ◽  
Negative Sequence Current ◽  
Short Circuit Fault

The intention of fault detection is to detect the fault at the beginning stage and shut off the machine immediately to avoid motor failure due to the large fault current. In this work, an online fault diagnosis of stator interturn fault of a three-phase induction motor based on the concept of symmetrical components is presented. A mathematical model of an induction motor with turn fault is developed to interpret machine performance under fault. A Simulink model of a three-phase induction motor with stator interturn fault is created for extraction of sequence components of current and voltage. The negative sequence current can provide a decisive and rapid monitoring technique to detect stator interturn short circuit fault of the induction motor. The per unit change in negative sequence current with positive sequence current is the main fault indicator which is imported to neural network architecture. The output of the feedforward backpropagation neural network classifies the short circuit fault level of stator winding.

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