The evolution of industry Artificial intelligence (AI) technology in today’s manufacturing system has the potential to be extremely helpful in applications such as quality assurance, process optimization, and predictive maintenance. Particularly, predictive maintenance in manufacturing uses data to forecast potential failures and optimize maintenance schedules, leading to increased efficiency, productivity, and sustainability. Industrial AI is an interdisciplinary area of research, encompassing fields such as Machine Learning (ML), natural language processing and robotics. Association rule learning, a rule-based ML method for discovering interesting relationships between variables in large databases, has been widely used across business intelligent applications for decision-making. However, there are few studies on predictive maintenance in manufacturing systems. This paper proposes Apriori-Based learning in R for providing the predictive analytics to improve fault detection and support predictive maintenance, thereby enhancing production stability and efficiency. The case study is conducted to illustrate the feasibility of the proposed method is demonstrated.

Hsiao-Kang Lin

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Association Rule Learning for Predictive Maintenance in Industry AI