Abstract
This paper provides a framework used to conduct advanced analytics in an Industrial Internet of Things (IIoT) ecosystem for condition monitoring of smart networked and instrumented rotating pieces of equipment in power generation plants. A discussion of the main components that make a rotating machine “smart and networked” is provided. As data analytics is an essential component of an IIoT system, a discussion on different analytic approaches to analyze sensor data to monitor smart, instrumented, and networked rotating machines is discussed. These approaches are based on Machine Learning algorithms that can be used to identify patterns and to identify potential anomalies on a rotating piece of equipment. The analytic approaches derive knowledge through calculation of key performance indicators (KPIs) that are derived from analyzing sensor data from instrumented rotating pieces of equipment utilizing analytical methods such as statistical models, unsupervised clustering algorithms, and anomaly detection and projection algorithms. A presentation is given on how the KPI’s generated by the analytic methods translate into actionable knowledge used by a Service Engineer domain expert to address any anomalies in the rotating piece of equipment being monitored. Finally, a discussion on how a smart, instrumented, and networked rotating machine operates within the IIoT ecosystem is provided.
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Acknowledgment
The author wishes to express its gratitude to Alessandro Bongiovi from ABB to have acted as the domain expert in this study. The author also expresses its gratitude to E. Harper, M. Acharya, I. Ahmed, and K. Severin, researchers in ABB Corporate research for their feedback during this project.
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Dagnino, A. (2019). Analytics in the Industrial Internet of Things. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_12
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DOI: https://doi.org/10.1007/978-3-030-01057-7_12
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