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Predictive Maintenance of Boiler Feed Water Pumps Using SCADA Data.

Marek Moleda1,2, Alina Momot2, Dariusz Mrozek2

  • 1TAURON Wytwarzanie S.A., Promienna 51, 43-603 Jaworzno, Poland.

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|January 24, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an IoT-based method for early fault detection in boiler feed pumps using existing data. The predictive maintenance approach minimizes expert knowledge, enhancing energy sector efficiency.

Keywords:
Internet of ThingsSCADAanomaly detectionboiler feed pumppredictive maintenance

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Area of Science:

  • Energy Sector
  • Industrial IoT
  • Predictive Maintenance

Background:

  • Traditional maintenance in the energy sector is often reactive.
  • Identifying potential equipment failures before they occur is crucial for operational efficiency and cost reduction.
  • Existing control systems capture valuable data but often lack advanced analytical capabilities for predictive insights.

Purpose of the Study:

  • To propose and validate a method for early fault detection in boiler feed pumps.
  • To leverage existing measurement data for predictive maintenance.
  • To reduce reliance on extensive expert knowledge for failure identification.

Main Methods:

  • Utilizing Internet of Things (IoT) enabled data collection.
  • Implementing a regression-based model to detect deviations from normal operational states.
  • Analyzing real-world measurement data and events from a coal-fired power plant.

Main Results:

  • Successful early detection of potential faults in boiler feed pumps.
  • Demonstration of a predictive system operating on available data with minimal expert input.
  • Identification of specific events and failures detectable by the proposed model.

Conclusions:

  • IoT, big data, and cloud computing enable integration of control and IT systems for enhanced efficiency.
  • The proposed method offers a viable approach to predictive maintenance in the energy sector.
  • Advanced analytics applied to existing data can significantly improve operational oversight and prevent failures.