An explainable artificial intelligence model for predictive maintenance and spare parts optimization

Dereci U., TUZKAYA G.

Supply Chain Analytics, cilt.8, 2024 (Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 8
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.sca.2024.100078
  • Dergi Adı: Supply Chain Analytics
  • Derginin Tarandığı İndeksler: Scopus
  • Anahtar Kelimeler: Explainable Artificial Intelligence, Human-centric Maintenance, Machine Learning, Predictive Maintenance, Spare Parts Management
  • Marmara Üniversitesi Adresli: Evet

Özet

Maintenance strategies are vital for industrial and manufacturing systems. This study considers a proactive maintenance strategy and emphasizes using analytics and data science. We propose an Explainable Artificial Intelligence (XAI) methodology for predictive maintenance. The proposed method utilizes a machine learning project cycle and Python libraries to interpret the results using the Local Interpretable Model-agnostic Explanations (LIME) method. We also introduce an early concept of spare parts management, presenting insights from predictive maintenance outcomes and providing explanations for decision-makers to enhance their understanding of the influential factors behind predictions. This study demonstrates that utilizing machine learning models in predictive maintenance is highly beneficial; however, the binary outcomes of these models can be misunderstood by decision-makers. Detailed explanations provided to decision-makers will directly impact maintenance decisions and improve spare part management.