|Jain, Suneet: How to ensure safety and security during condition monitoring and predictive maintenance : a case study. |
Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik, Masterarbeit Nr. 6 (2018).
132 Seiten, englisch.
Effective communication from machines and embedded sensors, actuators in industries are crucial to achieve industrial digitalization. Efficient remote monitoring as well as maintenance methodologies helps to accomplish and transform the existing industries to Smart Factories. Monitoring and maintenance leads to the aggregation of the real-time data from sensors via different existing and new industrial communication protocols. Development of user-friendly interface allows remote Condition Monitoring (CM). Context aware analysis of real-time and historical data provides capability to accomplish active Predictive Maintenance (PdM). Both CM and PdM needs access to the machine process data, industrial net-work and communication layer. Furthermore, data flow between individual components from the Cyber-Physical System (CPS) components starting from the actual machine to the database or analyze engine to the real visualization is important. Security and safety aspects on the application, communication, network and data flow level should be considered. This thesis presents a case study on benefits of PdM and CM, the security and safety aspect of the system and the current challenges and improvements. Components of the CPS ecosystem are taken into consideration to further investigate the individual components which en-ables predictive maintenance and condition monitoring. Additionally, safety and security aspects of each component is analyzed. Moreover, the current challenges and the possible improvements of the PdM and CM systems are analyzed. Also, challenges and improvements regarding the components is taken into consideration. Finally, based on the research, possible improvements have been proposed and validated by the researcher. For the new digital era of secure and robust PdM 4.0, the improvements are vital references.
|Abteilung(en)||Universität Stuttgart, Institut für Softwaretechnologie, Software Engineering|
|Betreuer||Wagner, Prof. Stefan; Holecek, Thomas; Wang, Yang|
|Eingabedatum||24. Mai 2019|