Risk-Based Inspections Using Artificial Intelligence

Abstract: Port Authorities and Operators have evolved significantly with regards to maintenance practices. Moving away from reactive maintenance has been achieved by implementing preventive maintenance strategies.
Some have moved a step further by leveraging operational data coming from their equipment to implement condition-based maintenance or even predictive maintenance using advanced analytics tools.
But are we still facing failures? Do we still have risks related to other assets? And what about those assets that do not generate operational data?
Static assets in port infrastructures such as piers, mooring bollards, dolphins, and other marine structures are also critical and need to be managed properly. Most Port Authorities and Operators are mainly focusing on operational equipment, while other types of assets can also represent significant risks in terms of safety, environmental aspects or other impacts leading to costly downtime.
And what about data that is not operational? A picture, for example, can provide significant value if properly analyzed. Even the operators’ feedback can generate significant insights to help determine the asset’s health. Inspection results can also provide significant insights on the status of the assets.
Asset integrity, reliability and durability requires all types of data that needs to be analyzed properly. Engineers have developed a strong expertise in analysing this data and calculating the integrity of the asset to ensure its safe usage and proper maintenance. Knowing how much time we have until we need to repair or replace a pier for example, is of key importance! And making sure we perform optimal maintenance activities also helps in improving an asset’s health and durability.
By combining this advanced expertise that engineers in asset integrity, reliability and durability have developed over the years with advanced analytics, it is now possible to improve maintenance planning significantly. And if we add deep learning and artificial intelligence, it is now possible for a system to learn and provide recommendations based on that engineering expertise!
Image recognition for example, thought by specialized engineers can provide significant value to a machine learning system. Being able to have an AI system recognize automatically corrosion defects and concrete issues on a structure, just by analyzing a picture taken by an operator during his pre-operational tests, can help identify risks and avoid significant failures. This leads to being able to answer multiple questions such as how much time is left before the asset fails? When should an asset be replaced? Is it still safe to operate this asset? What would be the best CAPEX plan for the next 5 years?
In this presentation you will learn how to step into a new era of maintenance called Risk Based Maintenance. Combining digital transformation technology with engineering expertise allows management of the entire life cycle of all types of assets used in port operations. This allows, among other things, to maximize the use of existing assets, minimize their total cost of ownership, reduce related carbon emissions, reduce operational risks and of course, improve operations efficiency and safety.

How does this project / topic contribute to the advancement of the industry and profession?: This project contributes to industry advancement by integrating artificial intelligence with engineering expertise, elevating maintenance standards and fostering proactive risk management practices. By leveraging AI-driven insights, it enhances maintenance strategies, ensuring optimal asset performance and minimizing downtime. Through this fusion of technology and expertise, it propels the industry towards a new era of maintenance excellence.

Does the project / topic implement new and innovative techniques, materials, technologies, and delivery methods?: This project embodies innovation by exploring cutting-edge AI techniques tailored for maintenance optimization. It pioneers the implementation of advanced analytics to develop predictive, prescriptive and risk-based maintenance strategies, revolutionizing asset management practices. By harnessing AI capabilities to address the complexities of managing diverse asset data, it sets new benchmarks for innovation in the industry.

What was the most challenging aspect of your project / topic and how did you handle it to ensure success?: The most challenging aspect of this project lies in effectively managing the diverse array of data sources essential for comprehensive maintenance planning. Balancing considerations between operational and non-operational assets presents a significant hurdle, requiring meticulous analysis and interpretation of asset health indicators and strong expertise in asset integrity. Successfully overcoming these challenges ensures accurate decision-making and fosters the project's overall success.

Who is the target audience for this paper?: This presentation targets port authorities and operators who seek advancements in maintenance practices to enhance operational efficiency and mitigate risks effectively. It is tailored for professionals engaged in asset integrity and reliability, offering valuable insights into optimizing asset performance and minimizing downtime. Furthermore, it addresses stakeholders concerned with operational excellence and cost-effectiveness in port operations and capital investment planning.