available topics

Initial situation:
In the context of Building Information Modeling (BIM), the IFC format plays a central role in the digital representation of building data. However, IFC is only one of many information models used in modern industrial and infrastructure applications. Other models include cost models, process models, and sensor data models from building technology or process automation. One approach from civil engineering is the multi-model, which can be used to link these different models together. This multi-model forms the basis for simulations, as it maps the dependencies of the individual model elements on each other, thus enabling a realistic representation of processes and systems.

Task:
The bachelor thesis should investigate how a multi-model consisting of various information models (e.g., IFC, cost models, process models) can be implemented in Industry 4.0 using administration shells and submodels. In particular, the focus will be on the integration and linking of these models in order to perform a simulation that takes into account the dynamic interactions between the models.

Subtasks:

• 1. Explanation of the multi-model concept and the information models involved in the construction industry (IFC, cost models, process models).

• Explanation of the concept of the administration shell (AAS) and its role as a digital representation of a physical asset, as well as consideration of the submodels within the administration shell for modeling specific information areas (e.g., building technology, costs, maintenance).

• Investigation of how different information models (IFC, costs, processes, etc.) can be integrated into an administration shell.

• Analysis of methods for referencing and linking the models in an administrative shell.

• Proposals for the implementation of submodels and references to enable seamless integration and simulation.

• Exemplary implementation and linking of a simple IFC model with a cost model and a process model in an administrative shell.

This topic is suitable for a diploma thesis.

For reasons of cost and flexibility, more and more devices from the Industrial Internet of Things (IIoT) are being used in industrial environments, especially for retrofitted solutions. However, when using such resource-constrained devices, meeting industry-specific data quality requirements becomes a critical challenge, as inherent system heterogeneities compromise native data integrity. In particular, the use of IIoT sensors generates data streams of inconsistent data quality right at the data source.

In particular, varying precision, diverging sampling rates, and temporal asynchrony of the different sensors lead to problematic data quality. This hinders the use of this data, especially as training data for AI integration. The aim of this work is to

•     determine data stream operations for stabilizing the quality of the source data,
•     analyze which of these operations are already used in industrial environments,
•     develop a conceptual system design for the use of such operations for IIoT integration.

The work is divided into the following main areas:

• Literature research on existing methods and brief evaluation
• Analysis of methods used in industrial communication technologies
• Design of a system architecture for the implementation of cleaning techniques
• Documentation and critical evaluation of the findings.

The topic is suitable for a bachelor's thesis.

Initial situation:
In Industry 4.0, asset administration shells (AAS) are used as digital representations of physical assets, containing their properties, statuses, and lifecycle data and linking them to real-time data from operations. This approach enables precise management, monitoring, and optimization of plants and processes. In the field of building automation, the trend is increasingly moving toward intelligent, networked building management with sensor-based systems and automated controls. If the building itself is viewed as an Industry 4.0 asset, new opportunities open up for the integration of real-time data, maintenance information, energy consumption data, and environmental parameters into a comprehensive digital administration shell. The aim of this thesis is to investigate how the Industry 4.0 administration shell approach can be transferred to building automation in order to view the building as an asset whose digital representation (the administration shell) integrates all relevant information for operation, maintenance, and optimization.

Task:
The bachelor thesis should demonstrate how a building can be represented as an Industry 4.0 asset with an administration shell (AAS). In particular, the integration of live data (e.g., from sensors and actuators), the linking of building components, and the life cycle data of the building should be considered. The goal is to create a digital twin of the building that enables precise and efficient management and optimization of building operations.

Subtasks:

• Analysis of the administration shell concept and its function as a digital representation of a physical asset, and description of submodels and their use for modeling specific information within the administration shell (e.g., energy data, building automation, sensor values).
• Investigation of how a building can be viewed as an Industry 4.0 asset and which building components and systems (heating, ventilation, air conditioning, lighting, security systems) can be integrated into an administration shell, possibly as submodels.
• Research into best practices and existing projects in which administration shells have been successfully used in the field of building automation, as well as analysis of relevant norms and standards.
• Development of submodels for specific building areas such as energy consumption, maintenance management, environmental data, and operating data.

• Investigation of the integration of live data from building automation, such as sensor values (temperature, humidity, air quality), energy consumption data, and status information from actuators.   

• Practical implementation (optional): Implementation of a prototype administration shell for a building that integrates exemplary data sources (e.g., from HVAC systems, lighting, energy consumption) and maps them in an administration shell.

This topic is suitable for both a master's thesis and a bachelor's thesis (with limited practical implementation).

Initial situation:
In modern industrial applications, IoT sensors play a key role in capturing a wide range of measurement data. The quality of the data collected often has a decisive influence on the accuracy and efficiency of processes, for example in predictive maintenance, production control, or quality control. Faulty, inaccurate, or incomplete data can lead to wrong decisions, resulting in costly downtime or quality defects. Therefore, ensuring high data quality in IoT sensors is becoming increasingly important.

Subtasks:

• Introduction to IoT sensors and their significance for industrial applications:
• Overview of common IoT sensors and their areas of application, as well as an explanation of the role of IoT sensors in industrial systems (e.g., automation, process monitoring, smart manufacturing).
• Clarification of the term “data quality” in the context of industrial IoT sensors and explanation of important quality characteristics of data (e.g., accuracy, completeness, consistency, timeliness, relevance, and reliability).
• Examination of typical sources of error in data collection that can impair the data quality of IoT sensors (e.g., calibration errors, drift, interference, network problems, sensor errors, environmental factors).
• Examination of existing solutions and best practices for classifying the data quality of various IIoT sensors.

• Development of an evaluation matrix for assessing data quality in IoT sensors (e.g., accuracy, reliability, response time, availability). 

• Classification of various existing IoT sensors in this matrix with justification.

This topic is suitable for a bachelor's thesis.