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

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.

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

Sustaining Power Resources through Energy Optimization and Engineering - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-4666-9755-3.ch013 ◽

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.433-435.705 ◽

2013 ◽

Vol 433-435 ◽

pp. 705-708 ◽

Cited By ~ 1

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.

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

Applied Sciences ◽

10.3390/app11010314 ◽

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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