Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Monocular Depth Estimation using a Multi-grid Attention-based Model
Volume-4 | Issue-3
Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
Volume-3 | Issue-3
Construction of Efficient Smart Voting Machine with Liveness Detection Module
Volume-3 | Issue-3
An Economical Robotic Arm for Playing Chess Using Visual Servoing
Volume-2 | Issue-3
Triplet loss for Chromosome Classification
Volume-4 | Issue-1
Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm
Volume-3 | Issue-4
Real Time Sign Language Recognition and Speech Generation
Volume-2 | Issue-2
Analysis of Artificial Intelligence based Image Classification Techniques
Volume-2 | Issue-1
Design of ANN Based Machine Learning Method for Crop Prediction
Volume-3 | Issue-3
A REVIEW ON IOT BASED MEDICAL IMAGING TECHNOLOGY FOR HEALTHCARE APPLICATIONS
Volume-1 | Issue-1
COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT
Volume-1 | Issue-1
Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields
Volume-3 | Issue-4
Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization
Volume-3 | Issue-4
State of Art Survey on Plant Leaf Disease Detection
Volume-4 | Issue-2
Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data
Volume-3 | Issue-4
OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry
Volume-3 | Issue-4
VIRTUAL RESTORATION OF DAMAGED ARCHEOLOGICAL ARTIFACTS OBTAINED FROM EXPEDITIONS USING 3D VISUALIZATION
Volume-1 | Issue-2
Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning
Volume-3 | Issue-4
Volume - 3 | Issue - 1 | march 2021
Published
12 April, 2021
This research article focuses on industrial applications to demonstrate the characterization of current and vibration analysis to diagnose the induction motor drive problems. Generally, the induction motor faults are detected by monitoring the current and proposed fine-tuned vibration frequency method. The stator short circuit fault, broken rotor bar fault, air gap eccentricity, and bearing fault are the common faults that occur in an induction motor. The detection process of the proposed method is based on sidebands around the supply frequency in the stator current signal and vibration. Moreover, it is very challenging to diagnose the problem that occur due to the complex electromagnetic and mechanical characteristics of an induction motor with vibration measures. The design of an accurate model to measure vibration and stator current is analyzed in this research article. The proposed method is showing how efficiently the root cause of the problem can be diagnosed by using the combination of current and vibration monitoring method. The proposed model is developed for induction motor and its circuit environment in MATLAB is verified to perform an accurate detection and diagnosis of motor fault parameters. All stator faults are turned to turn fault; further, the rotor-broken bar and eccentricity are structured in each test. The output response (torque and stator current) is simulated by using a modified winding procedure (MWP) approach by tuning the winding geometrical parameter. The proposed model in MATLAB Simulink environment is highly symmetrical, which can easily detect the signal component in fault frequencies that occur due to a slight variation and improper motor installation. Finally, this research article compares the other existing methods with proposed method.
KeywordsInduction Motor Faults Vibration Frequency Method
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