Industry 4.0 and Society 5.0 through Lens of Condition Based Maintenance (CBM) and Machine Learning of Artificial Intelligence (MLAI)

This paper provides preliminary discourse on buzz words about Industry 4.0 and Society 5.0. This discourse focuses on the lens of Condition Based Maintenance (CBM) and Machine Learning of Artificial Intelligence (MLAI). To some extent several companies have embarked Industry 4.0 and Society 5.0 within Internet of Things (IoT) technology. Through the wave of IoT Technology, Industries are adopting automated machinery. Predictive maintenance (PM) is indispensable not only toward the machines" vitality and longevity purpose, but also toward the human error reduction. This paper elaborates its discourse of Industry 4.0 and Society through the lens of CBM and MLAI. The mentioned Machine Learning, in this paper, refers to research methodology, as methodological frameworks. Those frameworks comprise several phases, which are: 1. Equipment Analysis; 2. Data Evaluation; 3. Data Selection and Process; 4. Modeling; 5. Decision Support Model Evaluation. The MLAI techniques are based upon the identification of behaviour patterns. This identification comprises datasets that exclude mathematical models or prior historical knowledge. The discourse in this paper intertwines CBM process and MLAI through data cleaning and processing, features stratification and extraction, model stratification and validation. This paper elaborates two renowned maintenance approaches which are preventive and corrective maintenance. Discourse in this paper focuses on corrective action, known as predictive maintenance (PM), or condition based maintenance (CBM) within Reliability Centered Maintenance (RCM). CBM is chosen as the most desirable strategy, as it involves the intervention as the consequence of the machine breakdown. It also provides cost savings toward spare parts consumption, and optimizes production.