The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions.
MULTIFILE
Optimization of aviation maintenance, repair, and overhaul (MRO) operations has been of high interest in recent years for both the knowledge institutions and the industrial community as a total of approximately $70 billion has been spent on MRO activities in 2018 which represents around 10% of an airline’s annual operational cost (IATA, 2019). Moreover, the aircraft MRO tasks vary from routine inspections to heavy overhauls and are typically characterized by unpredictable process times and material requirements. Especially nowadays due to the unprecedent COVID-19 crisis, the aviation sector is facing significant challenges, and the MRO companies strive to strengthen their competitive position and respond to the increasing demand for more efficient, cost-effective, and sustainable processes. Currently, most maintenance strategies employ preventive maintenance as an industrial standard, which is based on fixed and predetermined schedules. Preventive maintenance is a long-time preferred strategy, due to increased flight safety and relatively simple implementation (Phillips et al., 2010). However, its main drawback stems from the fact that the actual time of failure and the replacement interval of a component are hard to predict resulting in an inevitable suboptimal utilization of material and labor. This has two repercussions: first, the reduced availability of assets, the reduced capacity of maintenance facilities, and the increased costs for both the MRO provider and the operator. Second, the increased waste from an environmental standpoint, as the suboptimal use of assets, is also associated with wasted remaining lifetime for aircraft parts which are replaced, while this isn’t yet necessary (e.g., Nguyen et al., 2019).The recently introduced, condition-based maintenance (CBM) and predictive maintenance (PdM) data-driven strategies aim to reduce maintenance costs, maxi-mize availability, and contribute to sustainable operations by offering tailored pro-grams that can potentially result in optimally planned, just-in-time maintenance meaning reduction in material waste and unneeded inspections.
