Saipem 在其超深水钻井船 Saipem 12000 上实施了预测性维护系统，旨在提高运营效率和海上安全。

预测性维护利用实时数据和人工智能算法来监控设备状况，预测潜在故障，并在问题发生之前安排干预措施，从而减少停机时间和管理成本。

Saipem 12000是该公司钻井船队中首艘采用该系统的船舶。该系统由Saipem与ADC Energy合作开发，ADC Energy是一家专注于钻井平台和船舶安全保障的公司。持续的数据分析能够及时发现任何异常情况，并制定针对性的干预措施，从而提高可靠性和安全性。

萨伊佩姆7000号大型半潜式起重船也正在实施一项预测性维护项目。该项目重点关注船上电力生产的关键部件——柴油发电机组，利用物联网传感器和机器学习模型来检测潜在故障的早期迹象。这使得维护计划能够更加高效，并确保运营的连续性。

该系统由 BIP（一家专注于技术创新和数据科学的国际咨询公司）合作开发，将在未来几个月内进行测试。

Saipem implemented a predictive maintenance system on the Saipem 12000, its ultra-deepwater drillship, aimed at improving operational efficiency and offshore safety.

Predictive maintenance 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 Saipem 12000 is the first vessel in the company’s drilling fleet to adopt this system, developed in collaboration with ADC Energy, a company specialized in rig and vessel assurance. Continuous data analysis allows for the timely detection of any anomalies and the planning of targeted interventions, increasing reliability and safety.

A predictive maintenance project is also being implemented on the Saipem 7000, a large semisubmersible crane vessel. Focused on the 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 specializing in technological innovation and data science, the system will be tested in the coming months.