Research Interests
Research Interests
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Damage Detection
System Identification
Prognostics & Condition Monitoring
of Machinery
Structural Health Monitoring
Smart Wireless Sensors
Machine Vision & Deep Learning
Areas of Research
Areas of Research
AI-based Condition Monitoring
for Rotating Machinery
Developing bearing/gear fault detection methods using various feature extraction and classification
MED, PCA, SVM, Deep-learning and statistic features
Swarm-based Autonomous
Damage Identification
Using genetic and particle swarm intelligence to detect damages and localization
Digital Image Processing
Remote sensing & Image estimation using RANSAC algorithm
Wireless Sensor Network
Smart Wireless sensor and environmental monitoring system
Data-driven Monitoring
for Bearing Systems
Deep-learning-based rolling bearing fault detection method
Using machine learning & data science for IoT system diagnosis
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