LNG Armaturen Set

Development of a sensitive valve set for high-volume ship to ship LNG transfer

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The project aims at the development of a system which can be used on a large number of different ship types and thus leads to a significantly higher level of safety, installability and maintainability while at the same time reducing costs. The task of BIBA is to develop an Augmented Reality (AR) solution that can be used for maintenance and service purposes alongside the valve set.

By means of a combination of a commercial data goggle, a camera and an embedded PC, an easily configurable application solution is created. This solution should be able to identify the existing components, to read out the corresponding status information both visually and via radio, and to supply the users with maintenance information and checklists.

The AR solution will be developed to support technicians in operation, installation and maintenance of the sensitive LNG valve set. By means of image processing and object recognition techniques, the first step is to collect information on the condition of the valves. Subsequently, an AR-User Interface will be developed, which acts as an assistance system for the users.

Duration 01.03.2019 - 28.02.2021, Funded by BMWi
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LNG Safety

Safety process system for cryogenic fluid transfer

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The handling of cryogenic fluids (e.g. liquefied natural gas) bears major risks with regard to operational safety. If the liquid leaks during a transfer process (e.g. fueling of ships), large amounts of gas can quickly be produced which are highly flammable and explosive. Therefore, an appropriate safety system for process monitoring is necessary.

The aim of the project is to improve operational safety during the LNG transfer process by means of a redundant optical monitoring system. This system should be able to both detect fittings, ship superstructures, and people automatically and to perform an automated visual inspection of the correct coupling.

The multi-camera system consists of a wide-angle, a zoom and an infrared camera and can therefore react to a wide variety of environmental conditions (day, night, weather influences). It automatically monitors the LNG transfer process. By using Deep Machine Learning, the object recognition of fittings, ship superstructures and people is made possible, which is necessary for monitoring the danger zone.

Duration 01.03.2019 - 28.02.2021, Funded by BMWi
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Projektlogo Individual Predictive Maintenance


Individual Predictive Maintenance

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Ziel des Projektes ist die Entwicklung einer Toolbox zur Überwachung von Sensordaten für eine individuelle prädiktive Instandhaltung von Dieselmotoren für Schienenfahrzeuge.


Derzeit werden Instandhaltungsmaßnahmen reaktiv oder in periodischen Intervallen präventiv durchgeführt. Dieses Vorgehen ist jedoch mit hohen Kosten verbunden, da im Schadensfall meist Folgeschäden auftreten. Zudem führen die ausgefallenen Züge nicht nur zu Verspätungen der darin transportierten Personen und Güter, sondern blockieren auch die Bahnstrecke für weitere Transporte und die damit zusammenhängende Logistikkette. Allerdings ergeben sich durch das vorsorgliche Austauschen der Komponenten relativ hohe Instandhaltungskosten, da diese noch für einen längeren Zeitraum hätten genutzt werden können.


Durch eine Instandhaltung im Bedarfsfall (kurz vor Störereignis) können die Instandhaltungskosten minimiert werden, ohne das Risiko eines Zugausfalls signifikant zu erhöhen. Unter Anwendung künstlicher Intelligenz sollen frühzeitig auszutauschende Motorkomponenten identifiziert und damit eine ressourceneffiziente Instandhaltungsplanung ermöglicht werden.

Duration 01.02.2019 - 31.07.2020, Funded by BAB Bremer Aufbau-Bank GmbH

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Examining the influence of „Industry 4.0“ on Factory Layout Planning

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In the context of ongoing industrial digitization, new technologies are introduced to make production processes more efficient.

However, the digital data, which are created and processed by cyber-physical systems, promise improvements as well for adjacent areas, e.g. the restructuring of factories. Thus, the trend towards "Industry 4.0" influences factory planning and requires adapted methods for this task.

In this project, the prospects of digitized factory planning are identified and examined, based on a literature study as well as an empirical investigation .

The results will provide a methodical guideline for state-of-the-art factory layout planning and identify further research areas for adaptable digital factories.

Duration 20.11.2018 - 31.10.2019, Funded by Bremer Landesprogramm

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Projektlogo Intelligente Informationstechnologien für Prozessoptimierung und -automatisierung im Binnenhafen


Intelligent Information Technologies for Process Optimization and Automation in Inland Ports

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In Binntelligent, digital services as well as intelligent processes, procedures and information technologies for the optimization of trimodal logistics and transhipment processes in inland ports and the improved collaboration between inland and seaports are designed, implemented and evaluated in the field of application. It creates a cross-company visibility and transparency of decision-relevant information that allows predicting events in the supply chain.

For this purpose, an information system for (semi-) automated information distribution, operative process support and predictions will be developed. In addition to event predictions, forecasting capability in inland ports is achieved by simulation-based optimization of trimodal transhipment, which processes real-time real data and enables adaptability in synchro-modal freight traffic. Binntelligent considers logistics processes for containers and bulk goods in inland ports as well as the pre- and post-carriages. The planned technologies are designed for use in the Weser and Mittelland Canal shipping areas with the ports of Hanover, Braunschweig, Bremen and Bremerhaven and will subsequently be implemented for application-oriented testing and evaluation.

Duration 01.10.2018 - 30.09.2021, Funded by BMVI

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Arbeit Neu Denken: III
25. Februar 2019, Bremerhaven
Arbeit Neu Denken: IV
11. März 2019, Bremerhaven
Supply Chain Day
April 11th, 2019, Bremen, Germany
transport logistic 2019
June 4th - 7th, 2019, Munich, Germany
July 22nd - 26th, 2019, BIBA

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