FAULT DIAGNOSIS OF A BRAOKEN ROTOR BAR IN AN INDUCTION MOTOR USING MOTOR CURRENT SIGNATURE ANALYSIS
Date
2023-03
Journal Title
Journal ISSN
Volume Title
Publisher
IOE Pulchowk Campus
Abstract
The project report entitled "Fault Diagnosis of a Broken Rotor Bar in an Induction Motor
using Motor Current Signature Analysis” reports about Motor Current Signature Analysis
which is a non-invasive and cost-effective method for the fault diagnosis and classification
of induction motors. This technique uses the current signals generated by the induction
motor during its operation to diagnose faults such as rotor and stator winding faults, bearing
faults, and misalignment faults. The process involves capturing the current waveform of
the induction motor, processing it using signal processing algorithms, and analyzing it to
identify the fault. The method is based on the principle that different faults generate
distinctive current signatures. The current signals generated by the healthy and faulty
induction motor, loaded with different loads, is captured using a current transformer, which
is connected to a data acquisition system MyDAQ. The captured data is then processed
using signal processing algorithms which is then processed to extract relevant features that
are used to train machine learning models. The result indicates that Naïve Bayes algorithm
was able to classify health condition of an induction motor with the accuracy of 94.4%.
The algorithms like SVM and Decision tree also performed well with an accuracy of 88.9%
and 91.7% respectively. The trained models can then be used to perform real-time fault
classification on the induction motor, providing valuable information for predictive
maintenance and condition monitoring
Description
Data analytic and prediction techniques has always been our subject of interest. The genesis
of an idea began from the engine health monitoring techniques that came across during the
classes of fault monitoring and diagnosis
Keywords
INDUCTION MOTOR