Abstract
Predictive maintenance has lately proved to be a useful tool for optimizing costs, performance and systems availability. Furthermore, the greater and more complex the system, the higher the benefit but also the less applied: Architectural, computational and complexity limitations have historically ballasted the adoption of predictive maintenance on the biggest systems. This has been especially true in military systems where the security and criticality of the operations do not accept uncertainty. This paper describes the work conducted in addressing these challenges, aiming to evaluate its applicability in a real scenario: It presents a specific design and development for an actual big and diverse ecosystem of equipment, proposing an semi-unsupervised predictive maintenance system. In addition, it depicts the solution deployment, test and technological adoption of real-world military operative environments and validates the applicability.
Highlights
Depending on the field, forecasting capabilities can be translated into different advantages such as economic benefits in stock markets or business development; the incremental increase in the capabilities of saving a life or improving the quality of life in medicine; operational superiority in defense and security
If we focus on the Predictive maintenance (PdM) of ships, the number of works reduce drastically considering that they are an asset of capital importance in the global transport of goods with more than 80% of global transport share [40] and require novel methods that optimize their operation due to their maintenance costs of around 10% of the operating cost [41,42,43]
The unified architecture has been implemented for two different scenarios, for two types of engines: Diesel engines for propulsion of BAM Maritime Action Vessels and diesel engines for power generation in F-100 frigates
Summary
Depending on the field, forecasting capabilities can be translated into different advantages such as economic benefits in stock markets or business development; the incremental increase in the capabilities of saving a life or improving the quality of life in medicine; operational superiority in defense and security. As benefits of predictive versus preventive maintenance, the following can be highlighted: reduction in the number of interventions on the equipment, reduction in downtime to perform maintenance tasks and costs minimization in maintenance tasks In this sense, the main advantage of predictive maintenance is its ability to anticipate the future state of the monitored system and, extend the useful life of its assets, as demonstrated in various industrial applications such as electrical equipment maintenance [2,3]. Today’s current computing capacity and advanced Machine Learning (ML) techniques render it possible to perform real-time monitoring of the system’s mechanical condition to predict when components will fail [1,4] In this sense, predictive maintenance is currently understood as preventive maintenance [5] conditioned to the current state of the system and to the predictions of the future state made from operation history.
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