A Predictive Maintenance System Design and Implementation for Intelligent Manufacturing

Cinar E., Kalay S., Saricicek İ.

MACHINES, cilt.10, sa.11, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 10 Sayı: 11
  • Basım Tarihi: 2022
  • Doi Numarası: 10.3390/machines10111006
  • Dergi Adı: MACHINES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: automated machine learning (AutoML), cyber-physical systems (CPSs), data augmentation, key performance indicators (KPIs), predictive maintenance (PdM)
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

The importance of predictive maintenance (PdM) programs has been recognized across many industries. Seamless integration of the PdM program into today's manufacturing execution systems requires a scalable and generic system design and a set of key performance indicators (KPIs) to make condition monitoring and PdM activities more effective. In this study, a new PdM system and its implementation are presented. KPIs and metrics are proposed and implemented during the design to enhance the system and the PdM performance monitoring needs. The proposed system has been tested in two independent use cases (autonomous transfer vehicle and electric motor) for condition monitoring applications to detect incipient equipment faults or operational anomalies. Machine learning-based data augmentation tools and models are introduced and automated with state-of-the-art AutoML and workflow automation technologies to increase the system's data collection and data-driven fault classification performance.