Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time

Ayvaz S., Alpay K.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.173, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 173
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.eswa.2021.114598
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Anahtar Kelimeler: Predictive maintenance, Internet of things, Manufacturing systems, Artificial intelligence, Machine learning, Big data, ABSOLUTE ERROR MAE, INDUSTRY 4.0, ANOMALY DETECTION, BIG DATA, OPPORTUNITIES, ANALYTICS, PARADIGM, RMSE, TOOL
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

In this study, a data driven predictive maintenance system was developed for production lines in manufacturing. By utilizing the data generated from IoT sensors in real-time, the system aims to detect signals for potential failures before they occur by using machine learning methods. Consequently, it helps address the issues by notifying operators early such that preventive actions can be taken prior to a production stop. In current study, the effectiveness of the system was also assessed using real-world manufacturing system IoT data. The evaluation results indicated that the predictive maintenance system was successful in identifying the indicators of potential failures and it can help prevent some production stops from happening. The findings of comparative evaluations of machine learning algorithms indicated that models of Random Forest, a bagging ensemble algorithm, and XGBoost, a boosting method, appeared to outperform the individual algorithms in the assessment. The best performing machine learning models in this study have been integrated into the production system in the factory.