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 Symmetrical Loss of Excitation Fault Diagnosis in an Asynchronized High-Voltage Generator

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3054 ◽  
Author(s):  
Yanling Lv ◽  
Yuting Gao ◽  
Jian Zhang ◽  
Chenmin Deng ◽  
Shiqiang Hou
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
High Voltage ◽  
Short Circuit ◽  
Open Circuit ◽  
Voltage Generator ◽  
Three Phase ◽  
Circuit Loss ◽  
High Voltage Generator ◽  
Short Circuit Fault

As a new type of generator, an asynchronized high-voltage generator has the characteristics of an asynchronous generator and high voltage generator. The effect of the loss of an excitation fault for an asynchronized high-voltage generator and its fault diagnosis technique are still in the research stage. Firstly, a finite element model of the asynchronized high-voltage generator considering the field-circuit-movement coupling is established. Secondly, the three phase short-circuit loss of excitation fault, three phase open-circuit loss of excitation fault, and three phase short-circuit fault on the stator side are analyzed by the simulation method that is applied abroad at present. The fault phenomenon under the stator three phase short-circuit fault is similar to that under the three phase short-circuit loss of excitation. Then, a symmetrical loss of the excitation fault diagnosis system based on wavelet packet analysis and the Back Propagation neural network (BP neural network) is established. At last, we confirm that this system can eliminate the interference of the stator three phase short-circuit fault, accurately diagnose the symmetrical loss of the excitation fault, and judge the type of symmetrical loss of the excitation fault. It saves time to find the fault cause and improves the stability of system operation.

Recent Techniques to Identify the Stator Fault Diagnosis in Three Phase Induction Motor

2012 ◽  
Vol 1 (4) ◽  
pp. 106-120
Author(s):  
K. Vinoth Kumar ◽  
S. Suresh Kumar ◽  
A. Immanuel Selvakumar ◽  
R. Saravana Kumar
Keyword(s):  
Fault Diagnosis ◽  
Life Span ◽  
Induction Motors ◽  
Early Stage ◽  
Decomposition Technique ◽  
Maintenance Schedule ◽  
Three Phase ◽  
Symmetrical Component ◽  
Stator Fault ◽  
Financial Losses

Induction motors have gained its popularity as a suitable industrial workhorse, due to its ruggedness and reliability. With time, these workhorses are susceptible to faults, some are incipient and some are major. Such fault can be catastrophic, if unattended and may develop serious problems that may lead to shutdown to the machine causing production and financial losses. To avoid such breakdown, an early stage prognosis can help in preparing the maintenance schedule, which will lead to an improve life span. Scientist and engineers worked with different scheme to diagnose the machine faults. The authors diagnose the turn-to-turn faults condition of the stator through symmetrical component analysis. The results of the analysis are also verified through Power Decomposition Technique (PDT) in Matlab /SIMULINK. The results are compatible with the published results for known faults.

scholarly journals An accurate inter-turn short circuit faults model dedicated to induction motors

2021 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Fatima Babaa ◽  
Ouafae Bennis
Keyword(s):  
Induction Motors ◽  
Model Simulation ◽  
Short Circuit ◽  
Induction Machine ◽  
Industrial Systems ◽  
Proposed Model ◽  
Three Phase ◽  
Fault Prognosis ◽  
Short Circuits ◽  
Short Circuit Fault

Safety, disponibility and continuity of industrial systems are major issue in maintenance. In the last decades, these points are the important axes in the field of research. In fact, in many industrial processes research has picked up a fervent place and a particular importance in the area of fault diagnosis of electrical machines, in fact, a fault prognosis has become almost indispensable. The need of a mathematical model of three-phase induction machine, suitable for the simulation of machines behaviour under fault conditions, has received considerable attention. The paper presents a new practical and more precise model for induction motors after introducing inter turn short circuits faults. The proposed model is based on coupled magnetic circuit theory, capable to take into account any electrical asymmetry conditions. To verify the exactitude and the effectiveness of the model, simulation results for induction machine under interturn short circuit fault are presented. In spite of its simplicity, the proposed model is able to provide useful indications for diagnostic purposes. Experimental study is presented at the end of the paper to show that the proposed model predicts the induction machine behavior with a good accuracy.

Recent Techniques to Identify the Stator Fault Diagnosis in Three Phase Induction Motor

2016 ◽  
pp. 309-325
Author(s):  
K. Vinoth Kumar ◽  
S. Suresh Kumar ◽  
A. Immanuel Selvakumar ◽  
R. Saravana Kumar
Keyword(s):  
Fault Diagnosis ◽  
Life Span ◽  
Induction Motor ◽  
Induction Motors ◽  
Early Stage ◽  
Decomposition Technique ◽  
Maintenance Schedule ◽  
Three Phase ◽  
Stator Fault ◽  
Financial Losses

Induction motors have gained its popularity as most suitable industrial workhorse, due to its ruggedness and reliability. With the passage of time, these workhorses are susceptible to faults, some are incipient and some are major. Such fault can be catastrophic, if unattended and may develop serious problem that may lead to shut down the machine causing production and financial losses. To avoid such breakdown, an early stage prognosis can help in preparing the maintenance schedule, which will lead to improve its life span. Scientist and engineers worked with different scheme to diagnose the machine faults. In this paper, the authors diagnose the turn-to-turn faults condition of the stator through symmetrical component analysis. The results of the analysis also verified through Power Decomposition Technique (PDT) in Matlab /SIMULINK. The results are compatible with the published results for known faults.

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.

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