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Smart Maintenance Services for Buildings with Digital Twins and Augmented Reality
Published in Ibrahim Yitmen, BIM-enabled Cognitive Computing for Smart Built Environment, 2021
Stephan Embers, Patrick Herbers, Markus König, Mario Wolf, Sven Zentgraf
Furthermore, it is important that after maintenance is performed or anything in the building has changed, the Digital Twin is updated. When kept up to date, either through manual input or the smart maintenance system itself, the Digital Twin can be the single source of truth for all information about the real building it represents. With this knowledge, a cognitive building could tap into its smart maintenance system to monitor building components through Digital Twin. The smart maintenance system could automatically schedule maintenance tasks, either for preventive maintenance or reactive maintenance. In some cases, the intelligent maintenance system could even completely automate the maintenance process, for example, by using robotics. These concepts are still in the early stages of development and are far from being practical applications. Nevertheless, smart maintenance is the oil that keeps a cognitive building running. In other words, intelligent maintenance is an integral part of designing and operating a cognitive building.
Reliability-based cyber plant
Published in Stein Haugen, Anne Barros, Coen van Gulijk, Trond Kongsvik, Jan Erik Vinnem, Safety and Reliability – Safe Societies in a Changing World, 2018
Harald Rødseth, Per Schjølberg, Ragnhild Eleftheriadis, Odd Myklebust
CPS plant position analytics in level IV with deep knowledge. It has been pointed by the European commission that intelligent maintenance systems based on condition prediction mechanisms with computation of remaining useful life (RUL) will increase reliability availability and safety (EFFRA, 2013). Furthermore, more sophisticated techniques for cause-effect and trend analyses are also required. The deep analytics has been developed an integrated approach form machine learning and the need for zero defect manufacturing (ZDM). With intelligent sensor system ZDM can be operated for short term, medium term and long term decisions in the EU-project IFaCOM (intelligent fault correction and self-optimizing manufacturing systems) (Rødseth et al., 2016a). It has also been argued that maintenance could be one part of the IFaCOM concept. When advancing towards novel predictive maintenance technologies with reliability-based maintenance approaches, it is pointed out that this should include quality-maintenance methods as well as failure modes, effects, and criticality analysis (FMECA) (European Commision, 2016). Thus it is in this article of interest to investigate how FMECA can be balanced with big data analytics such as machine learning.
Development of a flexible data management system, to implement predictive maintenance in the Industry 4.0 context
Published in International Journal of Production Research, 2023
Vincent Ciancio, Lazhar Homri, Jean-Yves Dantan, Ali Siadat
Consequently, this research work proposes a full methodology to develop an Intelligent Maintenance System (IMS) that will help deploying operationally PdM. In addition to this methodology, the practical implementation of the system is detailed, as well as the tools included in the platform. The system is confronted to real industrial cases, in the context of automotive industry. One of the major contributions of this work is the proposition of an IMS that is non-domain centric, and flexible enough to be improved and tailored to the potential specificities of the industrial context in which it is used.