FLEet managemenT optimization through I4.0 enabled smart maintenance (FLET4.0)
Projece cofunded by European Union – SIF, Ricerca e
The research activities described in the design proposal focus on the service support process.
The objective is the development of a system to manage information obtained through Health Monitor and Fleet Mangement activities integrated with data from the depot (waste and repair rates), logistical data (lead time supplies and repair work), engineering data (modifications and manuals) to develop a landing strategy that has the least impact on fleet operation, reduce supply lead times, and optimize warehouse management.
The project intends to develop a new methodology approach to the process of maintenance outside the factory to provide operators new Internet-based tools for Internet Things, with the aim of maximizing the efficiency of the systems and make full use of the information available in the different areas of the manufacturing process.
It is expected to develop an enhanced reality environment along with a webservice architecture that integrates information from design and production departments and supports the employee in remote maintenance operations.
For the space domain, the satellite layout is constantly being observed with a plethora of sensors to prevent some component or sub-system from finishing in an unsuitable state of operation, if not deleterious, for the whole mission.
It will be defined a methodological process consisting of a set of algorithms and data analysis applications, aimed at continuously monitoring the health status of an orbiting platform (or a deep-space one) at the level of the individual component subsystems. The target system will provide an early identification of intervention needs and opportunities, in order to improve the failure isolation and troubleshooting processes.
In addition, it is equally important to define a plan for cross-fertilization among space, aeronautical and railway markets, precisely pivoting on the methodology of automatic health monitoring
Maintenance Planner Module. Development and implementation of an algorithm that provides a propulsion system removal plan maximizing engine availability to the end user and minimize time and cost of the maintenance activity.
Forecast & Strategy Module. Development of a Forecast & Strategy Module providing list of spare parts, scheduled according to their lead times, considering as input: Removal Plan, engineering data, historical production data.
Integrated Management System. A common working platform to optimize, harmonize and integrate everything with existing business software, thus creating a common ERP (Enterprise Resource Planning) system that optimizes the management of maintenance activities.
Service-Oriented Architecture (SOA). Creation of a Service-Oriented Architecture (SOA) that enables the maintenance process to be streamlined through the Internet’s technologies in the railway domain.
Ground systems for health monitoring of platforms and space sensors (orbiting or deep space). Automated systems for analyzing satellite telemetry flows and identifying pre-emptive behaviours (“patterns”) that can be identified as a symptom of possible anomalies (which could then manifest itself in subsequent phases). The expected tools in question are aimed at improving and simplifying the “industrial” process of monitoring the status of space systems.
The main benefit is the creation of an iterative simulation model that allows to integrate all the information described in order to provide a landing plans and logistic and strategic indications integrated with the planning of depot activities.
Project outputs will compose a technological environment through which a maintenance service to a remote client, using specific visual information transfer technologies can be provided. Outputs and interactions with the remote customer / maintainer system are made with technologies immersive, augmented, wearable and embedded devises.
The project will improve the methods currently in use by satellite operation engineers, considering that the number of parameters to be verified is constantly increasing, implementing an automatic control system on telemetry parameters self-learning the “normal” behavior of the satellite from recorded and available ground data.
The strategy involves improving the algorithms identified as a baseline in the analysis of abnormalities (and therefore predictive maintenance) and the study of their applicability and the study of the human-machine interface, which includes the evaluation of the MMI (Man- Machine Interface) that also allows end users to validate sessions
Project manager: Dott. Antonio Zilli, Distretto Tecnologico Aerospaziale Scarl
Scientific and Technology responsible: Prof. P. Pontarandolfo, Politecnico di Bari
Total cost: 7.5M€
Start and conclusion of the project 01/09/2018 – 28/02/2022
For more information email@example.com
Partners: DTA scarl, Blackshape Spa, ENGINSOFT Spa, Planetek srl, AvioAero srl, Politecnico di Bari, Università del Salento, Politecnico di Torino, Eka srl, Mermec Spa.