Date: 2025/-
Programs: NodeRed | Arduino IDE | Mosquitto MQTT
Technologies: MQTT | ESP32 micro controller | Raspberry Pi | IoT | NodeRed

About the Project
RedQueen is a privately developed smart home system that provides a modular, scalable solution for monitoring and controlling environmental conditions. The core idea was to use freely available hardware and open source software to create a powerful and customizable platform that is suitable for both private living spaces and technical application environments. The architecture is based on a central Node-RED server on a Raspberry Pi, which communicates via MQTT with a large number of standardized sensor and actuator modules based on ESP32. The aim was to achieve simple expandability while minimizing the programming effort through reusable code components and module designs. RedQueen enables the recording of environmental data such as temperature, humidity and CO₂ content as well as the control of a wide range of consumers - from plant lamps to irrigation systems and household appliances - via an intuitive dashboard. The system is actively operated and continuously expanded.

Technical Appropach
RedQueen's entire system architecture is designed as a distributed IoT system. A Raspberry Pi takes over the central control and visualization using Node-RED and MQTT-Broker. Communication is completely wireless via Wi-Fi. The end devices consist of modular ESP32 microcontrollers, which are quipped with various sensors (DHT22, SCD40, soil moisture sensors, etc.) and actuators (relays, MOSFETs, LED drivers). A high degree of standardization has been achieved through uniform firmware templates in the Arduino IDE, which facilitates both maintenance and scaling. The Node-RED dashboard serves as a central user interface for realtime monitoring, manual control and automated processes (e.g. time- or threshold-controlled).
All data points and control processes are mapped via MQTT in a hierarchical topic structure and can be integrated into Home Assistant or external systems if required.

Retrospective
RedQueen was born out of the desire to create a fully controllable and locally operated smart home system that works independently of cloud services. The modular structure of the system has proven to be extremely robust and flexible during operation. Particularly noteworthy is the simple reproducibility of the sensor modules, which means that new rooms or areas of application can be developed quickly. Thanks to the complete in-house development of all components - from hardware layouts to firmware and control logic - in-depth knowledge in areas such as embedded systems, network communication and IoT automation has been built up. Future enhancements include voice control integration, energy consumption monitoring and better long-term data storage and analysis. RedQueen remains an active project and also serves as an experimental platform for new control and sensor technologies.

Date: 2024/2025
Programs: Visual Studio Code | Siemens TIA Portal | EPLAN
Technologies: Python | Poetry | Github | OpenAI Whisper | Suno AI Bark | Argos Open Tech Translate | OPC UA | WiFi | Ethernet | Siemens PLC

About the project
The [vɔks] project developed a voice-controlled interface for use in special-purpose machines and modern manufacturing systems. The aim was to create a novel, user-friendly, and multilingual interface for human-machine interaction, particularly to support international teams in operation, maintenance, and monitoring. Voice control enables intuitive communication with machines and systems and can help to reduce the complexity of tasks for operators. A software prototype was developed based on state-of-the-art AI technologies and ran entirely locally — without the need for a connection to external servers. This also adequately addressed data protection concerns in industrial environments. The project combined technological innovation with practical applicability and made a significant contribution to the implementation of Industry 4.0 concepts.

Technical Approach
The prototype was built on a modular software architecture in Python and integrated advanced AI technologies such as OpenAI Whisper (speech-to-text), Argos Open Tech Translate (neural machine translation), and Suno AI Bark (text-to-speech). Communication with machine controls was handled via the open OPC UA standard. All processing was carried out locally to ensure the protection of sensitive data. The technological framework was implemented iteratively — initially as a proof of concept, and then further refined through evolutionary prototyping. Programmable logic controllers from Siemens were used to simulate industrial environments. A strong emphasis was placed on user-friendliness, which is why experts in UX/UI and production engineering were actively involved throughout the development process.

Retrospective
[vɔks] demonstrated a possible approach and showcased the potential of voice-controlled interfaces in industrial applications. The iterative development process enabled targeted implementation and allowed technological and conceptual challenges to be identified and addressed at an early stage. In the course of the project, it became clear that the local integration of AI components and user-centred adaptation were particularly important for success. The final evaluation of the prototype under realistic conditions revealed both its functionality and its potential for optimisation, for example in terms of response speed or the handling of complex queries. Overall, [vɔks] represents a successful step towards intuitive, secure and data protection-compliant voice-based human-machine communication.

Date: 2024
Programs: Microsoft Office, Zotero
Technologies: IoT/IIoT, CPS, Edge/Fog/Cloud Computing, AI, Machine Learning

About the Project
This State of the Art Report (STAR) on Knowledge Modeling & Sensors was created as part of a course for the Josef Ressel Center for Knowledge-based Visual Data Analysis in Industrial Production. The aim was to provide a well-founded analysis of current technologies, methods and challenges relating to the use of industrial data. The focus was on the role of intelligent sensor technology, structured data models and modern system architectures in the context of Industry 4.0, IIoT and machine learning. Due to the growing variety and volume of data in modern production environments, the ability to process and integrate data efficiently is becoming a key competence. Based on peer-reviewed publications, the report provides a structured analysis of relevant technologies for the acquisition, processing and use of industrial sensor data. The results help to lay the foundations for further research at the center and to prepare practical solutions for real industrial problems.

Technical Appropach
The report is based on a structured literature search conducted in April 2024 using scientific databases such as IEEE Xplore, ScienceDirect, Google Scholar, ResearchGate and the AI supported platform scienceOS. The search focused on publications from 2019 to 2024 and was based on topic-specific keywords. The relevance of the articles was systematically evaluated using defined exclusion criteria (e.g. language, availability, duplicates, subject-specificity). The selected publications were then analyzed according to four core categories:

1. sensor technology & data acquisition
2. data modeling
3. system architectures
4. performance & usability

This structured approach made it possible to establish cross-connections between the works and to draw a comprehensive picture of current technological developments.

Retrospective
Although it is not a classic development project, the STAR was an intensive analysis process with high relevance for application-oriented research. The literature review clearly showed how inconsistent and heterogeneous industrial data infrastructures still are today - both in terms of sensor integration as well as data models and system interfaces. The discussion of smart sensors, semantic data structures and distributed data processing via edge/fog/cloud concepts was particularly valuable. The report not only highlights current challenges, but also shows strategic solutions for effectively using data for predictive maintenance, efficiency analyses or automated decisions. As a contribution to the Josef Ressel Center, the work lays an important theoretical foundation for future research and practical projects.

Date: 2023/2024
Programs: Siemens TIA Portal | EPLAN | Visual Studio Code | Arduino DIE
Technologies: TCP/IP | Ethernet | Wi-Fi | ESP32 micro controller | Siemens PLC

About the Project
The “SC2SC” (abbreviation for Sensor Connector to Siemens Control) project aims to develop a flexible, costefficient data interface for connecting ESP32-based sensor modules to Siemens S7 PLC systems. In view of the significantly increasing relevance of individual IoT solutions in an industrial context, a TCP/IP-based communication between microcontrollers and programmable logic controllers was designed. The project was carried out as part of the Industry 4.0 Masterclass in collaboration with Mr. Philipp Kyr, BSc. and took place over a period of two semesters. The focus was not only on implementing the interface, but also on designing standardized processes to minimize user errors during integration. The project work thus forms the basis for modular extensions to industrial systems using freely configurable sensor technology and addresses key challenges such as interoperability, efficiency and safety in industrial communication.

Technical Appropach
The technical basis of the system is a specially developed communication protocol via TCP/IP. Communication is bidirectional between Siemens S7-1200/1500 controllers and various ESP32 models, which are connected via WLAN or Ethernet. A standardized data structure, which includes initialization, directive and sensor data headers, ensures consistency and expandability. In this context, the PLC acts as a data broker whose task is to analyze and process the incoming information in real time. Particular attention was paid to validating and securing the data by means of checksums (CRC), time stamps and user access control. Configuration is carried out via a user-friendly web interface on the ESP modules, making integration easier even for less experienced users. The project also integrates concepts for scalability, error detection and redundancy and was developed iteratively using evolutionary prototyping.

Retrospective
The system met the functional requirements and demonstrated stable communication in laboratory conditions. The prototype developed has the ability to reliably transmit sensor data to Siemens PLC systems, creating a solid foundation for industrial IoT applications. The implementation of the configuration interface and standardized data structures proved to be particularly successful and enables efficient integration of additional sensors. Problems in the initial configuration were solved through code optimization. The initial challenges in terms of network stability and hardware management were successfully addressed through the use of structured tests and iterative improvements. The project results offer significant potential for further developments, for example through the integration of additional protocols such as Profinet or cloud-based analysis modules. The simple expandability, low costs and openness make the concept interesting for retrofit applications and for extending existing systems with special solutions.

Date: 2022/2023
Programs: Beckhoff TwinCat3 | Siemens TIA Portal | EPLAN
Technologies: Beckhoff IPC | Siemens PLC | Soft-PLC | ProfiNET

About the Project
The „ForeSight“ project was carried out as part of the bachelor's thesis. The aim was to develop a software prototype for condition monitoring of electromechanical components in specialpurpose machines. The focus was on the efficient use of existing sensors and actuators without the need for costly hardware retrofits. The project was carried out in collaboration with INDAT GmbH, which ensured a practical approach. The integration of the prototype from the start of operation is intended to enable early fault detection in order to avoid unplanned downtime. The implementation was carried out using standardised function blocks in the Beckhoff TwinCAT 3 automation software and a soft PLC simulated on an industrial PC. This solution makes a significant contribution to increasing machine availability and safety in industrial environments. The work focuses primarily on small and medium-sized enterprises in order to offer a costefficient approach.

Technical Appropach
The technical implementation was carried out by developing modular function blocks in structured text language (ST) within the Beckhoff TwinCAT 3 environment. Two basic functions were implemented: a cycle counter for monitoring switching cycles and actuator controls, and a monitoring block for runtime analyses for more complex actuators such as motors, axis systems or actuating cylinders. The software prototype works with already installed sensors and operating data, liminating the need for additional hardware. In addition, test modules were created in the Siemens TIA Portal to simulate realistic process sequences. The development followed the evolutionary prototyping approach, whereby rough and detailed designs were iteratively refined and expanded. Early implementation allows initial states to be recorded, followed by evaluation through comparison of initialisation and monitoring values, in which treshold or maximum values, states and anomalies are recorded and analysed. The modular design allows for easy expandability for future machine concepts.

Retrospective
The project demonstrated that efficient condition monitoring can be achieved without costly monitoring systems and additional special sensors. Through the targeted use of existing signals, practical modules were created that can be applied in industrial environments. The monitoring modules developed provide important insights into switching cycles and operating times and can therefore be used for condition-based maintenance. Particularly noteworthy is the reusability of the modules in different machine configurations and the associated modularity of the solution. Challenges arose primarily in the design of universally applicable threshold values and the differentiated evaluation of complex machine states. Overall, the work lays a solid technical and theoretical foundation for further developments, such as cloud-based analyses or AI supported prediction models.

Date: 2021/2022
Programs: EPLAN | Siemens NX11 | Catia V5
Technologies: Computer-aided design | Finite element analysis (FEM/FEA) | Design Thinking

About the Project
As part of a two-semester dual project, the design and development of an affordable 2D CNC plasma cutting system for small and medium-sized enterprises (SMEs) was undertaken in collaboration with INDAT GmbH. The goal was to create a standardized in-house product that extends existing hand-held plasma cutting tools with a CNC quick-assembly system. This solution offers a cost-effective alternative to conventional CNC systems, particularly for companies with limited production space. During the first semester, the focus was on project initiation, the development of the electrical engineering concept, and comprehensive market and feasibility analyses. In the second semester, the project progressed to the detailed
mechanical design of a folding axis system, which was subsequently validated through finite element analysis (FEA). The project consistently emphasized practical implementation and economic viability, laying the foundation for a scalable, industrial-grade product suitable for market introduction.

Technical Appropach
The technical implementation was carried out in two consecutive phases. In the first project phase, the focus was on developing the electrotechnical concept, conducting a risk analysis, defining specifications, and assessing economic feasibility. This phase was supported by structured development tools such as mind maps, flow charts, and the quality gates method. In the second phase, attention shifted to the mechanical design, which was carried out using Siemens NX11 and CATIA V5. A central innovation in this phase was the development of a folding mechanism for the axis system, allowing for space-efficient storage and transportation of the machine. Customer requirements were continuously integrated using an iterative "design thinking" approach. In addition, extensive finite element analyses (FEA) were performed on safety-critical components to verify their load-bearing capacity. As a result, a functional and economically viable solution was developed that meets both technical demands and market requirements.

Retrospective
The project was a complete success: within two consecutive semesters, a thoroughly designed concept with realistic market opportunities was developed from a product idea. The combination of electrotechnical planning and mechanical design made it possible to work through all core areas of product development. The close cooperation with the company INDAT GmbH not only promoted technical knowledge, but also methodical and social skills. The application of modern methods such as quality gates and design thinking in a real company environment proved to be particularly valuable. The challenges, such as the acquisition of CATIA V5 or the integration of customer feedback, were mastered in a targeted and efficient manner. The project represents well-founded project work and offers a practical, marketable solution.