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Detecting operation regimes using unsupervised clustering with infected group labelling to improve machine diagnostics and prognostics

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  • Saari, Juhamatti
  • Odelius, Johan

Abstract

Estimating the stress level of components while operation modes are varying is a key issue for many prognostic models in condition monitoring. The identification of operation profiles during production is therefore important. Clustering condition monitoring data with regard to operation regimes will provide more detailed information about the variation of stress levels during production. The distribution of the operation regimes can then support prognostics by revealing the cause-and-effect relationship between the operation regimes and the wear level of components.

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  • Saari, Juhamatti & Odelius, Johan, 2018. "Detecting operation regimes using unsupervised clustering with infected group labelling to improve machine diagnostics and prognostics," Operations Research Perspectives, Elsevier, vol. 5(C), pages 232-244.
    • Handle: RePEc:eee:oprepe:v:5:y:2018:i:c:p:232-244
    DOI: 10.1016/j.orp.2018.08.002
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    References listed on IDEAS

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    Cited by:

    1. Aleksandra Grzesiek & Radosław Zimroz & Paweł Śliwiński & Norbert Gomolla & Agnieszka Wyłomańska, 2021. "A Method for Structure Breaking Point Detection in Engine Oil Pressure Data," Energies, MDPI, vol. 14(17), pages 1-24, September.
    2. Chiu, Singa Wang & Liang, Gang-Ming & Chiu, Yuan-Shyi Peter & Chiu, Tiffany, 2019. "Production planning incorporating issues of reliability and backlogging with service level constraint," Operations Research Perspectives, Elsevier, vol. 6(C).
    3. Chiu, Singa Wang & Chen, Hui-Cun & Wu, Hua-Yao & Chiu, Yuan-Shyi Peter, 2020. "A hybrid finite production rate system featuring random breakdown and rework," Operations Research Perspectives, Elsevier, vol. 7(C).
    4. Chiu, Yuan-Shyi Peter & Chiu, Victoria & Lin, Hong-Dar & Chang, Huei-Hsin, 2019. "Meeting multiproduct demand with a hybrid inventory replenishment system featuring quality reassurance," Operations Research Perspectives, Elsevier, vol. 6(C).

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