3 papers
cs.LG2025
Padé Approximant Neural Networks for Enhanced Electric Motor Fault Diagnosis Using Vibration and Acoustic Data
Sertac Kilickaya, Levent Eren
Purpose: The primary aim of this study is to enhance fault diagnosis in induction machines by leveraging the Padé Approximant Neuron (PAON) model. While accelerometers and microph…
cs.SD2024
Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description
Sertac Kilickaya, Mete Ahishali, Cansu Celebioglu +5
The frequent breakdowns and malfunctions of industrial equipment have driven increasing interest in utilizing cost-effective and easy-to-deploy sensors, such as microphones, for ef…
cs.LG2024
Thermal Image-based Fault Diagnosis in Induction Machines via Self-Organized Operational Neural Networks
Sertac Kilickaya, Cansu Celebioglu, Levent Eren +1
Condition monitoring of induction machines is crucial to prevent costly interruptions and equipment failure. Mechanical faults such as misalignment and rotor issues are among the m…