Summary
Maintenance processes applied on vessels and offshore platforms are obsolete. The technologies commonly applied to monitor machinery generate Gigabytes of technical data, requiring an expert to process it. Data cannot be handled by real-time monitoring services onshore, as the data connections available offshore are not designed for such flows. As a result, only 2% of the Mobile Drilling Units (MODUs) fleet in operation nowadays implement a real-time machinery monitoring, while the other 98% apply the out-dated Time-Based Maintenance (TBM) model. TBM increases lifecycle costs due to unexpected downtimes, higher labour costs and waste of parts in working condition. MODUs and platforms are bearing today unnecessary and excessive costs due to inefficient maintenance, even human injuries or environmental catastrophes are more likely to happen due to unmaintained machinery.
Our technology provides to vessels’ and platforms’ crews the possibility to monitor machinery health in real-time, allowing them to forecast and undertake the most appropriate actions. Foresight’s hardware is composed mainly by vibration monitoring equipment, that grant an easy installation onto any type of machinery. Foresight’s sensors continuously monitor the machinery, collect data, process them to reduce the size of the data packets and send them to the software on the cloud. Foresight Machine Learning (ML) module holistically processes the data gathered by sensors, synthesizing them into a comprehensive Health Index. It outperforms competitors in speed and reliability and is able to autonomously adapt and tailor its calculations on each machinery nominal behaviour.
Foresight relieves vessels and platforms maintenance costs by: (1) lowering the number of sensors needed; (2) reducing data communication needs; (3) removing the need for a technical expert onboard (4) minimising unexpected downtimes; (5) avoiding replacement of sound parts.
Our technology provides to vessels’ and platforms’ crews the possibility to monitor machinery health in real-time, allowing them to forecast and undertake the most appropriate actions. Foresight’s hardware is composed mainly by vibration monitoring equipment, that grant an easy installation onto any type of machinery. Foresight’s sensors continuously monitor the machinery, collect data, process them to reduce the size of the data packets and send them to the software on the cloud. Foresight Machine Learning (ML) module holistically processes the data gathered by sensors, synthesizing them into a comprehensive Health Index. It outperforms competitors in speed and reliability and is able to autonomously adapt and tailor its calculations on each machinery nominal behaviour.
Foresight relieves vessels and platforms maintenance costs by: (1) lowering the number of sensors needed; (2) reducing data communication needs; (3) removing the need for a technical expert onboard (4) minimising unexpected downtimes; (5) avoiding replacement of sound parts.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/867915 |
| Start date: | 01-04-2019 |
| End date: | 30-09-2019 |
| Total budget - Public funding: | 71 429,00 Euro - 50 000,00 Euro |
Cordis data
Original description
Maintenance processes applied on vessels and offshore platforms are obsolete. The technologies commonly applied to monitor machinery generate Gigabytes of technical data, requiring an expert to process it. Data cannot be handled by real-time monitoring services onshore, as the data connections available offshore are not designed for such flows. As a result, only 2% of the Mobile Drilling Units (MODUs) fleet in operation nowadays implement a real-time machinery monitoring, while the other 98% apply the out-dated Time-Based Maintenance (TBM) model. TBM increases lifecycle costs due to unexpected downtimes, higher labour costs and waste of parts in working condition. MODUs and platforms are bearing today unnecessary and excessive costs due to inefficient maintenance, even human injuries or environmental catastrophes are more likely to happen due to unmaintained machinery.Our technology provides to vessels’ and platforms’ crews the possibility to monitor machinery health in real-time, allowing them to forecast and undertake the most appropriate actions. Foresight’s hardware is composed mainly by vibration monitoring equipment, that grant an easy installation onto any type of machinery. Foresight’s sensors continuously monitor the machinery, collect data, process them to reduce the size of the data packets and send them to the software on the cloud. Foresight Machine Learning (ML) module holistically processes the data gathered by sensors, synthesizing them into a comprehensive Health Index. It outperforms competitors in speed and reliability and is able to autonomously adapt and tailor its calculations on each machinery nominal behaviour.
Foresight relieves vessels and platforms maintenance costs by: (1) lowering the number of sensors needed; (2) reducing data communication needs; (3) removing the need for a technical expert onboard (4) minimising unexpected downtimes; (5) avoiding replacement of sound parts.
Status
CLOSEDCall topic
EIC-SMEInst-2018-2020Update Date
27-10-2022
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