scholarly journals Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2392 ◽  
Author(s):  
Maciej Skowron ◽  
Marcin Wolkiewicz ◽  
Teresa Orlowska-Kowalska ◽  
Czeslaw T. Kowalski
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Induction Motor ◽  
Supply Voltage ◽  
Hopfield Network ◽  
Variable Load ◽  
Flux Analysis ◽  
Network Structures ◽  
Axial Flux ◽  
Rotor Winding

This paper presents a comparative study on the application of different neural network structures to early detection of electrical faults in induction motor drives. The diagnosis inference of the stator inter-turn short-circuits and broken rotor bars is based on the analysis of an axial flux of the induction motor. In order to automate the fault detection process, three different structures of neural networks were used: multi-layer perceptron, self-organizing Kohonen network and recursive Hopfield network. Tests were carried out for various levels of stator and rotor failures. In order to assess the sensitivity of the applied neural detectors, the tests were carried out for variable load conditions and for different values of the supply voltage frequency. Experimental results of the elaborated neural detectors are presented and discussed.

Detection and Diagnosis of Broken Rotor Bars in Induction Motors Using the Fuzzy Min-Max Neural Network

2012 ◽  
Vol 3 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Manjeevan Seera ◽  
Chee Peng Lim ◽  
Dahaman Ishak
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Induction Motor ◽  
Induction Motors ◽  
Fault Detection And Diagnosis ◽  
Broken Rotor Bars ◽  
Power Spectral ◽  
Motor Current Signature Analysis ◽  
Detection And Diagnosis ◽  
Current Signatures

In this paper, a fault detection and diagnosis system for induction motors using motor current signature analysis and the Fuzzy Min-Max (FMM) neural network is described. The finite element method is first employed to generate experimental data for predicting the changes in stator current signatures of an induction motor due to broken rotor bars. Then, a series real laboratory experiments is for broken rotor bars detection and diagnosis. The induction motor with broken rotor bars is operated under different load conditions. In all the experiments, the FMM network is used to learn and distinguish between normal and faulty states of the induction motor based on the input features extracted from the power spectral density. The experimental results positively demonstrate that the FMM network is useful for fault detection and diagnosis of broken rotor bars in induction motors.

scholarly journals Efficiency of Cascaded Neural Networks in Detecting Initial Damage to Induction Motor Electric Windings

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1314
Author(s):  
Maciej Skowron ◽  
Teresa Orłowska-Kowalska
Keyword(s):  
Neural Networks ◽  
Induction Motor ◽  
High Efficiency ◽  
Supply Voltage ◽  
Frequency Converter ◽  
Damage Classification ◽  
Voltage Frequency ◽  
Rotor Winding ◽  
Cage Induction Motor ◽  
Diagnostic Software

This article presents the efficiency of using cascaded neural structures in the process of detecting damage to electrical circuits in a squirrel cage induction motor (IM) supplied from a frequency converter. The authors present the idea of a sequential connection of classic neural structures to increase the efficiency of damage classification and detection presented by individual neural structures, especially in the initial phase of single or multiple electrical failures. The easily measurable axial flux signal is used as a source of diagnostic information. The developed cascaded neural networks are implemented in the measurement and diagnostic software made in the LabVIEW environment. The results of the experimental research on a 1.5 kW IM supplied by an industrial frequency converter confirm the high efficiency of the use of the developed cascaded neural structures in the detection of incipient stator and rotor winding faults, namely inter-turn stator winding short circuits and broken rotor bars, as well as mixed failures in the entire range of changes of the load torque and supply voltage frequency.

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.

scholarly journals STATIC AIR-GAP ECCENTRICITY FAULT DETECTION OF INDUCTION MOTOR USING ARTIFICIAL NEURAL NETWORK (ANN)

2020 ◽  
Vol 8 (8) ◽  
pp. 377-385
Author(s):  
KHALED MOHAMMED BIR GAMAL ◽  
SUPRIYA P. PANDA ◽  
M. V. RAMANA MURTHY
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Induction Motor ◽  
Back Propagation ◽  
Air Gap ◽  
Operating Conditions ◽  
Detection Techniques ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Induction motor plays an important role in the industrial, commercial and residential industries, owing to its immense advantages over the opposite types of motors. Such motors have to operate under different operating conditions that cause engine degradation leading to fault occurrences. There are numerous fault detection techniques available. There are numerous fault detection techniques available. The technique used in this paper to prove the effect of static air gap eccentricity on behaving or performing of the three-phase induction motor is the artificial neural network (ANN) as ANN depends on detecting the fault on the amplitude of positive and negative harmonics of frequencies. In this paper, we used two motors to achieve real malfunctions and to get the required data and for three different load tests. In this paper, we adopted MCSA to detect the fault based on the stator current. The ANN training algorithm used in this paper is back propagation and feed forward. The inputs of ANN are the speed and the amplitudes of the positive and the negative harmonics, and the type of fault is the output. To distinguish between healthy and faulty motor, the input data of ANN are well-trained via experiments test. The methodology applied in this paper was MATLAB and present how we can distinguish between healthy and faulty motor.

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