Fault Analysis And Predictive Maintenance Of Induction Motor Using Machine Learning
This work addresses predictive maintenance for industrial induction motors to prevent equipment damage, but it is incremental as it applies existing machine learning methods to a specific domain.
The paper tackles fault detection and classification in induction motors by developing a fast forward artificial neural network model that uses three-phase voltages and currents as inputs, achieving accurate real-time detection and classification of common electrical faults like overvoltage and ground fault on a 0.33 HP motor.
Induction motors are one of the most crucial electrical equipment and are extensively used in industries in a wide range of applications. This paper presents a machine learning model for the fault detection and classification of induction 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 artificial neural network model to detect some of the commonly occurring electrical faults like overvoltage, under voltage, single phasing, 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 0.33 HP induction 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.