Analytics in the Industrial Internet of Things View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-11-08

AUTHORS

Aldo Dagnino

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. More... »

PAGES

138-150

References to SciGraph publications

  • 2002. Enhancing Effectiveness of Outlier Detections for Low Density Patterns in ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING
  • 2009. A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data in ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING
  • Book

    TITLE

    Intelligent Systems and Applications

    ISBN

    978-3-030-01056-0
    978-3-030-01057-7

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-030-01057-7_12

    DOI

    http://dx.doi.org/10.1007/978-3-030-01057-7_12

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1109772490


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