A growing range of factors will put pressure on actors to extend the lifecycles of assets, including new building or construction costs, and a scarcity of certain raw materials. There will then be pressure to demonstrate sufficient structural integrity to continue safe operations by deploying advanced sensor technology in combination with advanced analytical models, as well as using 3D printing to repair and or replace failed parts.
Life extension of assets in the oil & gas as maritime sectors will be aided and driven through simulation technology, sensor data and new repair methods. The deeper use of real-time sensor data for real time monitoring will allow for a more detailed understanding of asset conditions. This may eliminate the need for costly non-destructive examination of structures, and where actions required for repair or refurbishment can be planned in due time. Information from sensors will also be used to optimize processes and will be crucial for the success of self-driving cars and autonomous ships. However, the information from sensors must be coupled with better models for determination of materials properties and predicting future degradation.
The ship, offshore and energy sectors, and especially the wind industry face a general challenge related to numerous false alarms and the challenge and cost this pose to maintenance for the operators. Hence, information flow from IoT, sensors, failure statistics, metocean data and operational data will still see a lot of focus also the next 10. Quality assurance and organisational maturity assessment are already required in class systematics to facilitate reliable decision support and handling of data. Standardisation of data formats and retrieval of data to shore is also facilitating this.
Sensor data will also support new design and materials solutions where the industry will have less operational data, where it is a need to shift from an experience-based process to a more holistic knowledge-based one for materials design and implementation. Faced with this challenge, use of sensor data, digital-twin models and virtual test laboratories will be important technologies for meeting future needs and support the increasing trend to build systems for long design lives of 50–100 years will become a common requirement for infrastructures and energy assets.
This emerging trend related to reuse of infrastructure is being explored in studies aimed at transporting hydrogen and CO2 in existing offshore pipelines. Other assets of interest will be converting oil & gas facilities post-cessation of production to capture wind, solar, wave and tidal current energies. Using sensor data for real-time monitoring will allow much deeper understanding of an asset’s condition, from both the structural response and material resistance side, with the aim of safe operation and life extension. Ageing assets, such as civil infrastructure, chemical plants and power plants are requiring new approaches to evaluation of materials for lifetime extension and simulation of existing and new potential failure modes far into the future.
What lies ahead?The use of digital-twin models (e.g. design models) in combination with sensor data for sound health monitoring recommendations for operational purposes may be necessity to document adequate life-extension for a structure or component. In near future large direct calculations can be performed without computational limitations and transferring sensor data by 5G in real-time. The sensor data will to an increasing degree also be correlated with a lot of other parameters including inspection findings through machine learning to improve the assessment of a risk of an asset or a specific system and pinpoint causes to problems. This will be a resource for decision support to do the right things, from follow up a poor workmanship at a yard to improved inspection of a specific asset or to improved design.
Sensor technologies are emerging with smart solutions, and in 2030 smart materials with built-in sensor properties will be widespread as a tool to monitor and predict an asset condition in real time. Sensor data will also facilitate smart ships to reduce need for crew, reduce opex while improving safety. Steering assist is an example on cars, and remote and autonomous operations of ship systems will increase.
Main author: Agnes Marie Horn
Editor: Mark Irvine
