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Predictive maintenance evaluates the condition of equipment by performing periodic (offline) or continuous (online) equipment condition monitoring.The ultimate goal of the approach is to perform maintenance at a scheduled point in time when the maintenance activity is most cost-effective and before the equipment loses performance within a threshold.
An intelligent maintenance system is a system that uses data analysis and decision support tools to predict and prevent the potential failure of machines. The recent advancement in information technology, computers, and electronics have facilitated the design and implementation of such systems.
AIOps tools use big data analytics, machine learning algorithms, and predictive analytics to detect anomalies, correlate events, and provide proactive insights. This automation reduces the burden on IT teams, allowing them to focus on strategic tasks rather than routine operational issues.
Predictive maintenance techniques are designed to help determine the condition of in-service equipment in order to estimate when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance , because tasks are performed only when warranted.
MindSphere is an industrial IoT-as-a-service solution [1] developed by Siemens for applications in the context of the Internet of Things (). [2] MindSphere stores operational data and makes it accessible through digital applications (“MindSphere applications”) to allow industrial customers to make decisions based on valuable factual information. [3]
KONUX was founded in 2014 by Andreas Loy (previously Kunze), Dennis Humhal, Vlad Lata, and Maximilian Hasler. The four students from different disciplines had met at their alma mater, the Technical University of Munich (TUM), [1] where they developed the basic idea of using sensor data to increase the availability of industrial facilities through predictive maintenance.
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