Abstract: This paper provides a brief review of the methodological composition of Risk Based Inspection (RBI) and the application of the methodology for safeguarding hull integrity of offshore floating structures, with fatigue as primary degradation mechanism. The work has a distinct focus on the opportunities RBI has to offer in combination with Structural Health Monitoring. In order to provide a clear picture of the state of the art knowledge, the current practices and regulations are briefly discussed after which the RBI methodology is introduced, the differences in guidelines and applications discussed and an 8-step approach is proposed. Subsequently, the methodology is outlined as an instrument for determining the residual fatigue life and the inspection scope and –schedule and the methodological embedding within an Advisory Hull Monitoring System is discussed and proposed.
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 https://doi.org/10.3390/app10238348 LinkedIn: https://www.linkedin.com/in/john-bolte-0856134/
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