An AI-based Architecture Framework for Improving End-of-line Reliability Tests of Electric Motors

• Published in: IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society pp. 1 - 6
• Main Authors: Soyturk, Mujdat, Coskun, Kutalmis, Izmitlioglu, Onur, Tumer, Borahan, Gunes, Deniz, Saracoglu, Sinan, Bulut, Baris, Ketmen, Hasan Burak, Hanedar, Ismethan, Asan, Tasemir, Aydin, Eray
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17.10.2022
• Subjects: artificial intelligence; Computer architecture; electric motor failure detection; Hardware; Sensors; Signal processing; Software; Software reliability; test framework; Throughput
• ISSN: 2577-1647
• DOI: 10.1109/IECON49645.2022.9968853

End-of-line (EOL) tests are an important step to detect and respond to reliability issues that electric motors face. In addition to conventional signal processing methods to establish automated test systems, Artificial Intelligence (AI) and Machine Learning (ML) based methods in recent years, managed to become a major enabler for smart manufacturing thanks to advancements in hardware and software components. Inevitably, the importance of quality data made its way into considerations and requirements of automated fault detection and condition monitoring systems. In this regard, this study proposes an AI-based testing framework for electric motors. We provide information on the reasons of faults observed and a test procedure to detect them. We also give detailed specifications on hardware (sensors and data collection equipment), and provide a data architecture and analysis on properties of ML models that make sense to be used in such scenarios.