CNN Algorithm Based Fault Identification in Three Phase Induction Motor Using Artificial Intelligence Techniques

Authors

  • G. Shasikala  Assistant Professor, Department of Electrical and Electronic Engineering, Er. Perumal Manimekelai college of Engineering, Hosur, Tamil Nadu, India
  • M. Rahila Thaslim  Department of Electrical and Electronic Engineering, Er. Perumal Manimekelai college of Engineering, Hosur, Tamil Nadu, India
  • E. Aswini  Department of Electrical and Electronic Engineering, Er. Perumal Manimekelai college of Engineering, Hosur, Tamil Nadu, India
  • S. Sasi  Department of Electrical and Electronic Engineering, Er. Perumal Manimekelai college of Engineering, Hosur, Tamil Nadu, India
  • M. Divya  Department of Electrical and Electronic Engineering, Er. Perumal Manimekelai college of Engineering, Hosur, Tamil Nadu, India

Keywords:

Motor, Fault Analysis, Machine Learning, CNN, Predictive Maintenance.

Abstract

The motors are one of the most crucial electrical equipment and are extensively used in industries in a wide range of applications. This project presents a machine learning model for the fault detection and classification of motor faults by using three phase voltages and currents as inputs. The aim of this work is to protect vital electrical components and to prevent abnormal event progression through early detection and diagnosis. This work presents a fast forward convolutional neural network model to detect some of the commonly occurring electrical faults like overvoltage, under voltage, unbalanced voltage, overload, ground fault. A separate model free monitoring system wherein the motor itself acts like a sensor is presented and the only monitored signals are the input given to the motor. Limits for current and voltage values are set for the faulty and healthy conditions, which is done by a classifier. Real time data from a motor is used to train and test the neural network. The model so developed analyses the voltage and current values given at a particular instant and classifies the data into no fault or the specific fault. The model is then interfaced with a real motor to accurately detect and classify the faults so that further necessary action can be taken.

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International Journal of Scientific Research in Science and Technology

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Published

2021-04-10

Issue

Volume 9, Issue 1, March-April-2021

Section

Research Articles

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How to Cite

[1]
G. Shasikala, M. Rahila Thaslim, E. Aswini, S. Sasi, M. Divya, " CNN Algorithm Based Fault Identification in Three Phase Induction Motor Using Artificial Intelligence Techniques, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 9, Issue 1, pp.1225-1246, March-April-2021.