KPI based performance estimation in production systems using deep learning techniques Derin öğrenme teknikleri kullanılarak üretim sistemlerinde KPI tabanlı performans tahminleme

Akkurt T., Sarıçiçek İ.

Journal of the Faculty of Engineering and Architecture of Gazi University, vol.39, no.3, pp.1499-1507, 2024 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 39 Issue: 3
  • Publication Date: 2024
  • Doi Number: 10.17341/gazimmfd.1100614
  • Journal Name: Journal of the Faculty of Engineering and Architecture of Gazi University
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Art Source, Compendex, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.1499-1507
  • Keywords: deep learning, Key performance indicators, light gradient boosting machines, long short term memory, performance estimation
  • Eskisehir Osmangazi University Affiliated: Yes

Abstract

Firms in the manufacturing sector need to constantly monitor their performance in order to maintain their development under competitive conditions in the market. In this study, eleven KPIs are determined to measure the production performance by taking into account the factory assets. The proposed system is designed in which the relevant KPIs are obtained via the instantaneous data received from the CNC machines in a production system. The main objective of this study is to measure and estimate production performance. In this way, it is aimed to provide a proactive approach for the assets whose performance is monitored by the decision-makers. LSTM and LightGBM models which are deep learning techniques are proposed for the estimation of performance indicators. The approximately three-month time series OEE (Overall Equipment Effectiveness) values of the sample CNC machine are used for estimation. The estimation performance of methods is compared over performance metrics (MSE, MAE, etc.). The results indicated that LightGBM outperforms LSTM for all performance metrics.