Faouzi Derbel
Biography
Prof. Faouzi Derbel is Professor for Smart Diagnostic and Online Monitoring at the Leipzig University of Applied Sciences in Germany and Senior Member of IEEE. He received his Dipl.-Ing. degree in Electrical Engineering and Information Technology from the Technical University of Munich and his Dr.-Ing. degree from the University of the Bundeswehr Munich.
From 2000 to 2012 he was in different positions in the industrial area e.g. strategic product manager and systems engineer, responsible for wireless and future technologies within Siemens Building Technologies in Munich (Germany) as well as Head of Research and Development within Siemens Building technologies in Muhlhausen (Germany) and QUNDIS Advanced Measuring Solutions (Germany).
In 2006, together with his team, he received the worldwide "Top+ Award for Product Innovation" within Siemens AG for the Wireless Sub-Metering Solution, and in 2009 the "Product Success Story Award" from Texas Instruments. Prof. Derbel is member of the board of directors of the Association of Short Range Devices Manufacturers ISAD and member of ETSI ERM TG28 dealing with short range wireless devices within the European Telecommunication and Standardization Institute ETSI. He is also Deputy Chair of technical group Smart Cities within the DKE "Deutsche Kommission Elektrotechnik, Elektronik und Informationstechnik im DIN und VDE". He is involved as General Co-Chair of many national and international conferences, e.g. IEEE I2MTC 2025 International Instrumentation and Measurement Technology Conference, Chemnitz. Prof. Derbel is also reviewer of many research programs e.g. HORIZON-EIC-2021-PATHFINDER OPEN.
His research focuses on the development of distributed self-powered sensor and measurement systems, including efficient signal processing methods for condition monitoring and state estimation of technical assets and smart grids.
Title
Sensor Integrating Machine Elements
Abstract
The monitoring of quality and efficiency remains a core objective within the industrial sector, fundamentally relying on the reliable and safe operation of machinery and infrastructure. Achieving these goals relies on the implementation of diverse maintenance strategies that ensure continuous and effective system performance. Traditional maintenance practices, such as reactive (event-driven) and preventive approaches, have long been employed. However, as the industrial landscape continues to develop, there is a growing focus on predictive maintenance. This approach is becoming especially crucial in the context of Industry 4.0 and the widespread adoption of the Internet of Things (IoT).
The transition towards predictive maintenance is necessitated by its potential to predict system failures before they occur, thus minimizing downtime and optimizing operational efficiency. To successfully implement predictive maintenance, the collection of high-quality data is essential. This necessitates the deployment of sensors capable of capturing accurate and meaningful data across various operational contexts.
In scenarios involving retrofitting or operation within harsh environmental conditions, the deployment of these sensors poses unique challenges. Reliable data acquisition in such demanding scenarios can only be efficiently accomplished through the integration of energy-autonomous wireless sensors. The process of data acquisition using these advanced sensors involves careful consideration of multiple influencing factors. The collected data is subjected to processing and analysis, employing a range of analytical techniques to extract meaningful features. These features enable the accurate identification and prediction of potential system failures, thus supporting timely interventions to prevent malfunctions.
The strategic integration of such sensors facilitates the capture of measurements precisely at or near the point of origin. By aligning maintenance efforts more closely with actual system conditions, industries can achieve substantial improvements in operational efficiency and asset longevity, while simultaneously reducing maintenance costs and minimizing unscheduled downtimes.