The reliability and efficiency of electric motors are critical in industrial
applications, where unexpected faults can lead to costly downtime and
safety hazards. Traditional fault detection methods often require
extensive manual inspection and may not capture subtle anomalies in
motor behavior. Infrared thermography has emerged as a non-invasive
technique to detect temperature variations in motor components, which
can indicate potential faults. However, the challenge lies in accurately
classifying these faults to prevent failures. Current methods lack the
precision needed to classify various motor faults accurately and
quickly, especially when dealing with complex thermal patterns. The
integration of advanced deep learning architectures with feature
extraction techniques presents an opportunity to enhance the detection
and classification of motor faults. This study proposes a hybrid model
combining DenseNet, a deep learning architecture known for its high
performance in image analysis, with a Speeded-Up Robust Features
(SURF)-based Artificial Neural Network (ANN) for feature extraction
and classification. Infrared thermography images of motors were first
processed through DenseNet for initial feature extraction. The SURF
algorithm further refined these features, which were then classified
using ANN. The model was trained and validated on a dataset of
infrared thermography images, representing various motor fault
conditions, including bearing wear, misalignment, and insulation
failure. The proposed model achieved an overall accuracy of 98.7% in
detecting and classifying motor faults, outperforming traditional
methods by 5.2%. The model also demonstrated high sensitivity (97.8%)
and specificity (99.1%) in identifying subtle temperature variations
indicative of early-stage faults. These results highlight the effectiveness
of the DenseNet and SURF-based ANN approach in enhancing the
reliability of motor fault detection using infrared thermography.
B. Sasikumar, P. Rajendran
Knowledge Institute of Technology, India
Fault Detection, DenseNet, SURF-based ANN, Infrared Thermography, Deep Learning
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 15 , Issue: 1 , Pages: 3366 - 3374 )
Date of Publication :
August 2024
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