Modeling a predictive maintenance management architectureto meet industry 4.0 requirements: A case study

Abstract

Industry 4.0 is the latest paradigm of industrial production enabling a new level of organizing and controlling the entire value chain within a product life cycle by creating a dynamic and real-time understanding of cross-company behaviors. It is expected to have a considerable impact in the oil and gas (O&G) sector by revolutionizing current predictive maintenance and operation optimization. There are several challenges to be overcome before the Industry 4.0 vision is achieved: a standardized reference architecture, a business model, robust services, and products are all lacking. This paper develops a reference architecture for an intelligent maintenance management system that complies with Industry 4.0 visions and requirements. The industrial needs were derived from stakeholders and use case scenarios using a case study methodology. Systems engineering methods were applied to transfer the needs of the existing maintenance management system into a desired functional architecture. The new and upgraded requirements are predominantly related to advanced data analytics, resulting in new and modified functions within the traditional “Reporting” and “Analyses” modules. A more complex maintenance program is created through interfaces between various enabled data categories (historical records, real-time measurements of performance and health, expert-just-in-time). The study points to the changes required in the classical O&G maintenance management process to comply with Industry 4.0 vision and requirements.