Application of the Novel Wavelet Ann Method for Segregating Bearing Faults in Three-phase Induction Motor | Chapter 2 | Current Approaches in Engineering Research and Technology Vol. 10

Induction motors are a critical component of many industrial processes and are frequently integrated into commercially available equipment. Safety, reliability, efficiency, and performance are some of the major concerns of induction motors.

Three-phase induction motors are the ‘workhorses’ of industry and are the most widely used electrical machines. For this reason, the detection of motor failures is very important. Bearing problems are one of the major causes of drive failures. Early detection of bearing faults allows replacements of the bearings rather than replacement of the motor. The present contribution reports experimental results for monitoring bearing faults in induction motors. Motor line currents have been analyzed using modern signal processing and data reduction tools combining Park’s Transformation and Discrete Wavelet Transform (DWT). Feed Forward Artificial Neural (FFANN) based data classification tool is used for fault characterization based on DWT features extracted from Park’s Current Vector Pattern. An online algorithm is tested successfully on a three-phase induction motor and experimental results are presented to demonstrate the effectiveness of the proposed method which can reliably distinguish the inner race and outer race defects of the bearing. It is observed that for five processing elements in the hidden layer, 100% classification accuracy is achieved for healthy and faulty conditions.

Experimental results are presented to demonstrate the effectiveness of the proposed methodology. The study concluded that the proposed methodology is useful in identifying inner race and outer race defects of bearings in induction motors with 100   percent accuracy.

 

Author (s) Details

Anjali. U Jawadekar
Department of Electrical Engineering, S.S.G.M. College of Engineering Shegaon, India.

 

Mukesh Ravindra Chavan
Department of Electrical Engineering, S.S.G.M. College of Engineering Shegaon, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/caert/v10/2639

Application of the Novel Wavelet Ann Method for Segregating Bearing Faults in Three-phase Induction Motor | Chapter 2 | Current Approaches in Engineering Research and Technology Vol. 10

 

Induction motors are a critical component of many industrial processes and are frequently integrated into commercially available equipment. Safety, reliability, efficiency, and performance are some of the major concerns of induction motors.
Three-phase induction motors are the ‘workhorses’ of industry and are the most widely used electrical machines. For this reason, the detection of motor failures is very important. Bearing problems are one of the major causes of drive failures. Early detection of bearing faults allows replacements of the bearings rather than replacement of the motor. The present contribution reports experimental results for monitoring bearing faults in induction motors. Motor line currents have been analyzed using modern signal processing and data reduction tools combining Park’s Transformation and Discrete Wavelet Transform (DWT). Feed Forward Artificial Neural (FFANN) based data classification tool is used for fault characterization based on DWT features extracted from Park’s Current Vector Pattern. An online algorithm is tested successfully on a three-phase induction motor and experimental results are presented to demonstrate the effectiveness of the proposed method which can reliably distinguish the inner race and outer race defects of the bearing. It is observed that for five processing elements in the hidden layer, 100% classification accuracy is achieved for healthy and faulty conditions.
Experimental results are presented to demonstrate the effectiveness of the proposed methodology. The study concluded that the proposed methodology is useful in identifying inner race and outer race defects of bearings in induction motors with 100   percent accuracy.

 

Author(s)details:-

 

Dr. Anjali. U Jawadekar
Department of Electrical Engineering, S.S.G.M. College of Engineering Shegaon, India.

 

Mukesh Ravindra Chavan
Department of Electrical Engineering, S.S.G.M. College of Engineering Shegaon, India.

 

Please See the book here :- https://doi.org/10.9734/bpi/caert/v10/2

Fault Verdict in Multi Phase Induction Machine using Intelligence Evolution Computation Algorithm Optimized Neural Network | Chapter 8 | Technological Innovation in Engineering Research Vol. 3

This research presents a new solution strategy for recognising defects in a multiphase induction motor using the least mean square filter (LMS) and a new hybrid neural network with mind evolution computation algorithm. The use of an artificial neural network (ANN) has shown to be a useful tool for solving difficulties in a variety of fields. Teaching an artificial neural network (ANN) is one of the most complex processes in system learning, and it has lately attracted a huge number of researchers. The proposed hybrid fault diagnostic technique includes an efficient feature extractor based on LMS and a fault classifier based on a hybrid neural network. Testing 600 samples modelled on the failure model determines the performance and efficiency of the offered hybrid neural network classifier. The average correct classification with and without the mind evolution computation technique is roughly 98 percent and 96.17 percent, respectively, for various defect signals. The findings of the simulation research show that the proposed hybrid neural network is effective for fault detection in multiphase induction motors.

Author(s) Details:

A. Balamurugan,
Department of Electrical and Electronics Engineering, Ariyalur Engineering College, Ariyalur, Tamil Nadu, India.

R. Ramya,
Department of Electronics and Communication Engineering, Kings Engineering College, Chennai, Tamil Nadu, India.

S. Saravanan,
Principal, Ranipet Institute of Technology, Walaja, Vellore, Tamil Nadu, India.

Please see the link here: https://stm.bookpi.org/TIER-V3/article/view/7065

Fault Tolerant Control Design for Linear Systems with Appropriate Performance Degradation Using Eigen Structure Assignment | Chapter 4 | Advanced Aspects of Engineering Research Vol. 10

 Using multiple model techniques, the main aim is to design a fault-tolerant control with reasonable performance degradation due to faults in actuators, sensors, and device dynamics. Reference models, often known as acceptable output reference models, are used to convey the achievable performance under various component failures. A series of controllers is synthesised based on these models using eigen structure assignment. The appropriate controller is reconfigured for a specific fault state, and revised command input is selected automatically to achieve the desired output. The IMM estimator is used for fault identification and diagnosis, and eigen structure assignment is used for controller reconfiguration. The aircraft model was selected to show the model's efficacy.

Author (s) Details

S. Kanthalakshmi
Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, India

M. Raghappriya
Department of Electronics and Instrumentation Engineering, Government College of Technology, Coimbatore, India

R. Latha
Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, India

View Book :- https://stm.bookpi.org/AAER-V10/article/view/753

Faults Classification in Analog Circuits Using Nearest Neighbours of Fault Variables | Chapter 10 | Emerging Trends in Engineering Research and Technology Vol. 8

 Faults classification using nearest neighbour estimate of fault variables of circuit under test (CUT) is

proposed. Fault variables corresponding to the components of CUT are found from the fault free,
faulty circuit parameters and test vectors of the components. The proposed method does not require
any test point or node selection techniques for fault diagnosis. The faults classification technique is
tested using benchmark circuits like Sallen Key Band Pass Filter.

Author(s) Details

Dr. G. Puvaneswari
Department of ECE, Coimbatore Institute of Technology, Coimbatore, India.

View Book :- http://bp.bookpi.org/index.php/bpi/catalog/book/228