Partial discharge detection for stator winding insulation of motors using artificial neural network

Abstract

In this article, the insulation fault detection of high-voltage motors by the artificial neural network algorithm is used. The proposed method can evaluate the status of operating motor without interrupting the normal operation. According to the measurement of partial discharge information, this research establishes the relationship of stator failures and pattern features. This study uses common high-voltage motor stator fault types to experimentally produce four types of stator test models with insulation defects; these models are compared with a healthy motor model. Through the learning of the artificial neural network, the experimental results show that the artificial neural network-based stator fault diagnosis system proposed in this article has a recognition rate as high as 90% when the conjugate gradient algorithm is used, and there are 20 neurons in the hidden layer.

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.…”

Induction Motor Fault Detection and Classification using RCNN and SURF Based Machine Learning Algorithms and Infrared Thermography

“…But, this method will be hard to attain the precise models of faulty motors and also to apply model-based methods. Chen et.al [5] used an artificial neural network (ANN) for the fault detection of high-voltage motors. The relationship between stator failures and pattern features were established depending on the partial discharge (PD) information estimation.…”