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Focused on Naval

Forget about failures and unplanned downtimes and focus on running your business

Fleet Maintenance Plans

R&D Predictive Maintenance

O&M (Operation & Maintenance) Assessment

We analyze your needs.
We propose you alternative solutions to your problems.
We offer upgrade options that provide increased reliability of the vessel and lower cost of operation and maintenance.

Industrial inventory

It is required as a starting point of any maintenance plan. This list must be a complete inventory of facilities that permit linking maintenance actuations, history of failures of each element, spare parts, technical documentation, associated budgets, and more.

Lifecycle support

Throughout the life of a vessel, the maintenance system must be updated, the required inspections and design reviews must be performed and the necessary retrofitting at the right time has to be effected. Our technicians are trained to advice, design and monitor all these processes.

Supplies / stock management

We analyze your specific situation and we propose to you suitable solutions. We identified all your ships’ parts, we locate alternative suppliers for you and we help you to define and to manage the stock level more operational each time.

Maintenance consultancy

At any time we can look for technical solutions adapted to the questions regarded to maintenance. We define maintenance plans and we submit them for approval of the Statutory Certification. We design the schedule of inspections, we elaborate manuals and we apply, as an internal auditor, the method stablished by the ISM code, etc. .. With the approval of our clients, we study the applicability of the best options to solve every new challenge.

Contact us, tell us about you and your business, resources and objectives, and, find out the way we can help you improving your results account.

IDI PROJECT 20190292

New big data analysis and intelligent treatment model for predictive maintenance

The objective of the project was to develop a new intelligent predictive maintenance model based on Machine Learning techniques that allows detecting the causes and estimating the time until a failure appears. Thanks to this and the adaptation of the alarm thresholds of the different parts of the monitored equipment, decisions about maintenance can be made at the most appropriate time, also advising on the most recommended operating regime according to the current state of the equipment.

It combines different supervised and unsupervised learning algorithms with maintenance indicators (equipment parameters) chosen through a rigorous procedure and innovative, patented calculation models to estimate the causes of failures and their onset times.

It aims to change current maintenance models (preventive or corrective) for a predictive one that maximizes operating times and reduces repair periods. Furthermore, this model will be much more reliable over time, since with an increasing amount of data available, its predictions will be more accurate and closer to reality.

This project has been co-financed by the European Regional Development Fund (ERDF) through the Pluriregional Smart Growth Operational Programme. A way of making Europe.

Budget: 464,419.00 Euros.

CDTI Aid: Refundable loan: €290,261.87; Non-refundable loan: €104,494.28