9 citations · 26 across the 4 of their papers we have counts for
4 papers
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 micropho…
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…
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…
Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification
Sertac Kilickaya, Mete Ahishali, Fahad Sohrab +2
Hyperspectral image (HSI) classification is an important task in many applications, such as environmental monitoring, medical imaging, and land use/land cover (LULC) classification…