We organised a wonderful special session on 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 at the IEEE IECON 2022 conference in Brussels.
The exact title of the special session was “Predictive analytics architectures and applications for industrial systems – 2nd edition”.
The following papers were presented at the session:
- Condition Monitoring on Renewable Energy Production with Application to Wind Generation by Betül Sena Çağlar, Burak Ketmen, Barış Bulut
- IoT Architecture and Solutions for Predictive Maintenance of Mobile Machinery by Jani Hietala, Kalle Raunio, Tero Jokinen, Petri Kaarmila
- On Suitability of the Customized Measuring Device for Electric Motor by Rok Hribar, Gašper Petelin, Margarita Antoniou, Anton Biasizzo, Stanko Ciglarič, Gregor Papa
- An AI-based Architecture Framework for Improving End-of-line Reliability Tests of Electric Motors by Mujdat Soyturk, Kutalmış Coşkun, Onur Izmitlioglu, Borahan Tümer, Deniz Güneş, Sinan Saraçoğlu, Barış Bulut, Hasan Burak Ketmen, İsmethan Hanedar, Taşdemir Aşan, Eray Aydın
- Improved Domain Adaptation Approach for Bearing Fault Diagnosis by Sertac Kilickaya, Turker Ince, Levent Eren, Serkan Kiranyaz, Moncef Gabbouj, Ozer Can Devecioglu
- Improved Detection of Broken Rotor Bars by 1-D Self-ONNs by Levent Eren, Turker Ince, Murat Askar, Ozer Devecioglu
- Investigation of Potting Compounds on Thermal-Fatigue properties of Solder Interconnects by Leiming Du, Xiujuan Zhao, Piet Watte, Rene Poelma, Guoqi Zhang, Willem Driel
- An IoT Cloud and Big Data Architecture for the Maintenance of Home Appliances by Luis Ferreira, Tiago Fonseca, Orlando Sousa
- Data-Centric Model Development to Improve the CNN Classification of Defect Density SEM Images by Corinna Kofler, Claudia Anna Dohr, Judith Dohr, Anja Zernig
The details of the special session were as follows:
Organisations are continuously researching new strategies to improve the Overall Equipment Effectiveness (OEE) of their equipment, from highly complex and computerised industrial machines to refurbished equipment. This is now possible thanks to the high levels of automation and, particularly to innovative IoT solutions, the use of AI in manufacturing, PHM, digital twin and condition monitoring.
Particularly, maintenance costs vary between 15% and 70% of production or ownership costs, making maintenance an important cost to be reduced. Therefore, this session aims at addressing the main problems of designing industrial maintenance systems, from the collection of data from sensors to its analysis supported by advanced algorithms. The session targets all kinds of maintenance paradigms, from reactive to predictive and prescriptive, or any other kind of advanced maintenance concepts, with a strong focus on architectures and design.
Topics of interest include, but are not limited to:
- Architectures for predictive analytics applications
- Industrial applications and pilots on emerging maintenance techniques
- Sensors and Cyber Physical Systems for maintenance applications
- AI based control systems in advanced production
- Prognostic and Health Management (PHM) / digital twin / condition monitoring
- Machine learning algorithms
- Signal analysis algorithms for maintenance applications
- Diagnosis & Prognosis & Remaining useful life
- Maintenance safety
- New frontiers: Machine as a service, maintenance as a service and reliable manufacturing
Interested parties please contact baris [at] enforma-tr.com