Optimization of the feedforward neural network for rotor cage fault diagnosis in three-phase induction motors

2011 ◽  
Author(s):  
Z. M. Taibi ◽  
M. Hasni ◽  
S. Hamdani ◽  
O. Rahmani ◽  
O. Touhami ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Induction Motors ◽  
Feedforward Neural Network ◽  
Three Phase ◽  
Rotor Cage

Rotor cage fault diagnosis in three-phase induction motors based on a current and virtual flux approach

2009 ◽  
Vol 50 (4) ◽  
pp. 1026-1032 ◽  
Author(s):  
Dulce F. Pires ◽  
V. Fernão Pires ◽  
J.F. Martins ◽  
A.J. Pires
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Three Phase ◽  
Rotor Cage ◽  
Virtual Flux ◽  
A Current

Application of Probabilistic Neural Networks in Fault Diagnosis of Three-Phase Induction Motors

2013 ◽  
Vol 433-435 ◽  
pp. 705-708 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Induction Motors ◽  
Probabilistic Neural Network ◽  
Short Circuit ◽  
Model And Simulation ◽  
Fast Training ◽  
Three Phase ◽  
Short Circuits ◽  
Stator Fault

In fault diagnosis of three-phase induction motors, traditional methods usually fail because of the complex system of three-phase induction motors. Short circuit is a very common stator fault in all the faults of three-phase induction motors. Probabilistic neural network is a kind of artificial neural network which is widely used due to its fast training and simple structure. In this paper, the diagnosis method based on probabilistic neural network is proposed to deal with stator short circuits. First, the principle and structure of probabilistic neural network is studied in this paper. Second, the method of fault setting and fault feature extraction of three-phase induction motors is proposed on the basis of the fault diagnosis of stator short circuits. Then the establishment of the diagnosis model based on probabilistic neural network is illustrated with details. At last, training and simulation tests are done for the model. And simulation results show that this method is very practical with its high accuracy and fast speed.

Fault Diagnosis of Induction Motors Based on RBF Neural Network

2013 ◽  
Vol 462-463 ◽  
pp. 85-88 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Induction Motors ◽  
Rbf Neural Network ◽  
Short Circuit ◽  
Three Phase ◽  
Diagnosis Accuracy ◽  
Simulation Results

In order to improve the diagnosis accuracy of stator short circuit faults of three-phase induction motors, in this paper, a method using three-layered RBF neural network is proposed to diagnose the short circuit faults on the basis of analysis of structure and algorithm of RBF neural network. Then the approach to establish RBF neural network and the influence of different expanding coefficients upon the diagnosis accuracy are illustrated. The simulation results show that RBF neural network can successfully diagnose and classify six typical short circuit faults of induction motors. This method has a faster speed, higher accuracy and it needs fewer samples. In conclusion, RBF neural network is practical, efficient and intelligent in fault diagnosis of induction motors.

scholarly journals Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors

2020 ◽  
Vol 11 (1) ◽  
pp. 314
Author(s):  
Gustavo Henrique Bazan ◽  
Alessandro Goedtel ◽  
Marcelo Favoretto Castoldi ◽  
Wagner Fontes Godoy ◽  
Oscar Duque-Perez ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Mutual Information ◽  
Induction Motors ◽  
Operating Conditions ◽  
Industrial Systems ◽  
Bearing Fault Diagnosis ◽  
Bee Colony ◽  
Three Phase ◽  
C4.5 Decision Tree ◽  
The Time Domain

Three-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach.

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

2021 ◽  
Vol 12 (1) ◽  
pp. 597
Author(s):  
Ahmed Thamer Radhi ◽  
Wael Hussein Zayer
Keyword(s):  
Neural Network ◽  
Equivalent Circuit ◽  
High Speed ◽  
Fuzzy Neural Network ◽  
Induction Motors ◽  
Stator Winding ◽  
Three Phase ◽  
Fuzzy Neural ◽  
Diagnosis Method ◽  
Faults Diagnosis

The paper deals with faults diagnosis method proposed to detect the inter-turn and turn to earth short circuit in stator winding of three-phase high-speed solid rotor induction motors. This method based on negative sequence current of motor and fuzzy neural network algorithm. On the basis of analysis of 2-D electromagnet field in the solid rotor the rotor impedance has been derived to develop the solid rotor induction motor equivalent circuit. The motor equivalent circuit is simulated by MATLAB software to study and record the data for training and testing the proposed diagnosis method. The numerical results of proposed approach are evaluated using simulation of a three-phase high-speed solid-rotor induction motor of two-pole, 140 Hz. The results of simulation shows that the proposed diagnosis method is fast and efficient for detecting inter-turn and turn to earth faults in stator winding of high-speed solid-rotor induction motors with different faults conditions

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