Saipem tests AI-based predictive maintenance on deepwater drillship

The predictive maintenance system uses real-time data and artificial intelligence algorithms to monitor equipment conditions, predict potential failures, and schedule interventions before problems occur, thereby reducing downtime and management costs. The aim is to improve operational efficiency and safety.

Saipem 12000 is the first vessel in the company’s drilling fleet to adopt the technology, which was developed in collaboration with ADC Energy, a company specialising in rig and vessel assurance. 

Continuous data analysis allows anomalies to be detected quickly, enabling targeted interventions, increasing reliability and safety.

The pilot project on the drill ship combines Saipem’s technical expertise and ADC’s rig equipment and data science know-how, and is part of a broader process that aims to extend the use of artificial intelligence and data analysis to the entire fleet.

Aligned with this approach, a predictive maintenance project is also being implemented on Saipem 7000, one of the largest semi-submersible crane vessels in the world. 

Focusing on the vessel’s diesel generators, critical components for onboard power production, the project uses IoT sensors and machine learning models to detect early signs of potential failures. This allows maintenance to be planned more efficiently and ensures operational continuity. 

Developed in collaboration with BIP – an international consulting firm specialising in technological innovation and data science – the system installed on Saipem 7000 will be tested in the coming months.

Saipem said it is “committed to integrating artificial intelligence, predictive analytics, and advanced digital tools to make its offshore energy operations increasingly safe, efficient, and sustainable.”