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

Abstract

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.

Application of Machine Learning for Inter Turn Fault Detection in Pumping System

“…Mexbios development studio was built up to analyse the parameter changes in induction motor during fault condition. Though this is possible to implement in industrial applications, this process cannot predict the fault before the fault occurs and major damages are seen [31]. The proposed model is a simple easy process and helpful for less amount of data and predict the fault before massive failure.…”

A Fault Analysis Method for Three‐Phase Induction Motors Based on Spiking Neural P Systems

“…If a rule neuron contains a fault pulse, then the number of the neuron is numbered as 1; otherwise, it is 0. Fuse melt fault p 5 Damage of shaft seal ring structure p 6 Oil sealing material overheating p 7 Excessive roughness value of the seal surface shaft p 8 Excessive temperature p 9 Mechanical fault of the rotor winding p 10 e motor centerline is inconsistent with the pump one p 11 Fault of the bearing locking device p 12 Rotor core deformation p 13 Fracture or shedding of magnetic slot wedges p 14 Dewelding at the joint of the winding and lead wire p 15 Connection box joint loosened p 16 Poor contact of the power control loop switch p 17 Decrease in rotational speed p 18 Excessive current in a phase p 19 Excessive excitation current p 20 A e initial pulse value of input neurons and truth value of rule neurons are obtained via historical data and expert experience [23].…”

“…erefore, how to improve the abovementioned fault prediction and abductive fault diagnosis methods or put forward new ones is the main issue in the corresponding engineering domain for the motors. On the other hand, with the rapid development of artificial intelligence technology, intelligent analysis and diagnosis methods are gradually developed, such as expert systems (ESs) [15], artificial neural networks (ANNs) [16][17][18][19][20], Petri nets (PNs) [21][22][23], tissue P systems (TPSs) [24][25][26], and spiking neural P systems (SNPSs) [27][28][29][30][31][32][33][34]. Specifically, SNPS is a novel high-performance bioinspired distributed parallel computing model with powerful information processing ability.…”

Faults diagnosis in stator windings of high speed solid rotor induction motors using fuzzy neural network

“…In [17] a review of faults detection methods in PMSM are introduced also fault performance summarization of recently proposed algorithms are recorded. A stator winding inters turn fault detection method using current sequence component value to classify the fault with ANN has been presents in [18]. Stator winding fault detection method in PMSM based on empirical mode decomposition analysis of transient motor current was presents in [19] with finite elements method is used for model's simulation.…”