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

Juhamatti Saari and Johan Odelius

Operations Research Perspectives, 2018, vol. 5, issue C, 232-244

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.

Keywords: Maintenance; Operation regime; Clustering; Data mining; LHD (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:oprepe:v:5:y:2018:i:c:p:232-244

DOI: 10.1016/j.orp.2018.08.002

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