mAIntAIn

The objective of the project is the creation and commercial exploitation of mAIntAIn, a software platform that will optimize the monitoring and maintenance processes of industrial artifacts and components, aiming at these artifacts:

• • • accurate prediction of failures of their components,
    • the diagnosis of lesions that may arise and in which they are involved and as a result,
    • maximizing uptime while minimizing the possibility of failure

mAIntAIn comes to assist in the implementation of modern conservation strategies, using a combination of modern technologies (real estate internet – Internet of Things, cloud services, big data algorithms and machine learning technologies), providing a tool to predict the behavior of these artifacts and streamlining decisions for their maintenance or replacement.

The mAIntAIn platform can provide capabilities of both the most advanced predictive and prescribing maintenance strategies and the most established strategies, either in the software as a service model or in the form of a service.

The cornerstone of mAIntAIn’s technological innovation is machine learning algorithms and their combination with deterministic predictive models (eg: empirical models, simulations using numerical methods, etc.) of machine behavior. Using, (i) the flow of data from the sensors of the machines as they are served by cloud and real estate technologies, (ii) the empirical data and specifications of manufacturers and iii) the historical data as they result from the above accumulated flow, it becomes possible to implement the predictive and prescribing maintenance strategies, thus serving the basic objectives of mAIntAIn. At the same time, mAIntAIn can serve the rest of the maintenance strategies, thus leaving room for its users to implement any maintenance strategies at their discretion.  Especially in shipping, web accessibility technologies (eg: VSAT, LEO) have dramatically helped in the use and consolidation of real estate technologies, making the use of mAIntAIn as a logical next step for companies that already make use of the latter.

mAIntAIn comes to be implemented at a key point in time: the needs of the market for reducing operating costs, reducing the environmental footprint and increasing safety and robustness can be met with the synergy of a series of innovative technologies that can provide robust information on the current and future behavior of machines, assisting in decision making to a technician, operational and business level.

mAIntAIn comes to enhance the competitiveness of its users, offering a number of benefits. In particular, these are for:

Shipping companies: i) increase of fleet usage time due to timely troubleshooting of faults, reduced downtime due to repairs and increase of maintenance and replacement periods, ii) 24-hour technical support through the automated processes of the platform, reducing part of maintenance by the crew, iii) reduction of acquisition costs and increase of cost predictability over time and iv) the ability to monitor longer volume of machinery and/or ships by the heads of technical departments on land.

The manufacturers: i) greater accuracy in forecasting cash flow, ii) streamlined production planning of machines and their spare parts, iii) use and exploitation of data during the use of machines by the research and development department, thus promoting manufacturing quality and reducing its corresponding cost and iv) continuous contact with its customers.