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Random forest-based multi-faults classification modeling and analysis for intelligent centrifugal pump system

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Abstract

This paper proposes experimental results and a modeling procedure to analyze the factors affecting the defects of centrifugal pumps. The centrifugal pump is an essential mechanical element. Pump malfunction can cause problems in the entire system because most production facilities are equipped with various centrifugal pumps. Therefore, the diagnosis of pump failure is essential, and several studies have proposed data-driven methodologies for detecting faults in pump systems. Most studies have performed fault classification using only the vibration signal of the pump. However, this study approached the problem in three ways to improve FDC performance. Firstly, this study tried to collect various signals such as pressure, flow, and motor current as well as vibration signals. That is because various signals are likely to indicate the performance of the pump system considering the mechanical characteristics of the centrifugal pump. Secondly, this study presented a methodology for extracting features that can be used in machine learning models from the signals. For objective analysis, not only various statistics were calculated, but also features in the frequency domain were extracted. Using the extracted features, random forest modeling was applied and higher performance was achieved in experiments. Lastly, variables that significantly affected the anomaly detection of the pump were suggested. In order to analyze experimental results of feature importance, we have presented a procedure to introduce a correlation matrix of cosine similarity, which is a new method that has not been attempted in previous research.

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Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1A4A1030333). Also, this work was supported by Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (20217510100020, Development of platform process using common core and materialization technology for rare metal recovery from industrial low-grade waste liquid).

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Authors and Affiliations

  1. Department of Artificial Intelligence, College of Software, Jeju National University, Jeju, 63243, Korea

    Kyuchang Chang

  2. School of Mechanical Engineering, Chungnam National University, Daejeon, 34134, Korea

    Seung Hwan Park

Authors
  1. Kyuchang Chang
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  2. Seung Hwan Park
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Correspondence to Seung Hwan Park.

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The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Kyuchang Chang received Ph.D. degree in industrial engineering from Korea University in 2022. He is currently an Assistant Professor in the Department of Artificial Intelligence at Jeju National University in Korea. His research interests include big data analytics, smart diagnostics and prognostics in high-tech manufacturing. He is conducting research to improve manufacturing systems using the latest artificial intelligence technologies.

Seung Hwan Park received a Ph.D. degree in industrial management engineering from the Korea University in 2017. He worked as a data scientist in the SK Hynix from 2017 to 2018. In 2018, he joined the School of Mechanical Engineering, Chungnam National University, where he is currently an Associate Professor. His research interests include process monitoring, control and diagnostics for smart manufacturing, and data driven manufacturing analytics.

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Chang, K., Park, S.H. Random forest-based multi-faults classification modeling and analysis for intelligent centrifugal pump system. J Mech Sci Technol 38, 11–20 (2024). https://doi.org/10.1007/s12206-023-1202-2

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  • DOI: https://doi.org/10.1007/s12206-023-1202-2

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