Date: 08/06/2026
Hour: 14.00
Student Name & ID: Muhammed Faik Kandemir 255105117
Supervisor: Prof. Dr. Hüseyin Canbolat
Topic: AI-Based Fault Detection and Predictive Maintenance in Electric Motors and Electro-Mechanical Actuators
Link or Room: A516
Abstract:
Electric motors and electro-mechanical actuators are essential components widely used in industrial automation, defense industry, aerospace, and critical control systems. Unexpected faults in these systems may cause performance degradation, unplanned downtime, high maintenance costs, and reliability problems. Therefore, early fault detection and predictive maintenance approaches have become important research topics for improving system safety and operational continuity.
In this seminar, artificial intelligence-based fault detection and predictive maintenance in electric motors and electro-mechanical actuators will be discussed. Current and vibration data will be used to analyze different operating conditions and fault scenarios. In this context, signal processing methods such as time-domain analysis, frequency-domain analysis, Fast Fourier Transform (FFT), and feature extraction will be utilized. Then, machine learning and deep learning algorithms such as Support Vector Machine, Random Forest, Convolutional Neural Networks, Long Short-Term Memory, and Autoencoder-based models will be comparatively evaluated.
The main objective of this study is to examine suitable artificial intelligence models for reliable fault diagnosis in electric motors and electro-mechanical actuators and to present a computer-based intelligent monitoring approach for predictive maintenance applications.