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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.
Sensors (Basel, Switzerland)
|January 24, 2020
Summary
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.
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.
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