Dynamic criticality assessment as a supporting tool for knowledge retention to increase the efficiency and effectiveness of maintenance

Digitalisation offers industrial companies a multitude of opportunities and new technologies (e.g. Big Data Analytics, Cloud Computing, Internet of Things), but it still poses a great challenge for them. Especially the choice of the maintenance strategy, the increasing complexity and level of automation of assets and asset components have a decisive influence. Due to technological progress and the new possibilities offered by industry 4.0, the interaction of different systems and assets is essential to increase the efficiency of the maintenance processes within the value-added chain and to guarantee flexibility permanently. These factors lead to an increased importance of a process methodology for a dynamic evaluation of the asset’s condition over the entire life cycle and under changing framework and production conditions. Therefore, legal and environmentally relevant requirements are considered, based on the procedure of HAZOP (IEC 61882), and ensure the traceability of the results and systematically record the asset’s knowledge gained this way so that it is not tied to individual employees, as it is currently the case. The criticality assessment as a basic component of Lean Smart Maintenance, the dynamic learning, and knowledge-oriented maintenance, offers such a holistic, value-added oriented approach. A targeted optimization of the maintenance strategy is possible through automated evaluation of the assets and identification of the most critical ones based on company-specific criteria derived from the success factors of the company and considering all three management levels normative, strategic and operational. By considering the resource knowledge in the maintenance-strategy optimization process and using suitable methodologies of knowledge management based on the prevailing data quality for it, the efficiency and effectiveness are permanently guaranteed in the sense of continuous improvement

A Knowledge-Based Digital Lifecycle-Oriented Asset Optimisation

The digitalisation of the value chain promotes sophisticated virtual product models known as digital twins (DT) in all asset-life-cycle (ALC) phases. These models. however, fail on representing the entire phases of asset-life-cycle (ALC), and do not allow continuous life-cycle-costing (LCC). Hence, energy efficiency and resource optimisation across the entire circular value chain is neglected. This paper demonstrates how ALC optimisation can be achieved by incorporating all product life-cycle phases through the use of a RAMS²-toolbox and the generation of a knowledge-based DT. The benefits of the developed model are demonstrated in a simulation, considering RAMS2 (Reliability, Availability, Maintainability, Safety and Sustainability) and the linking of heterogeneous data, with the help of a dynamic Bayesian network (DBN)