Predictive Maintenance Study for High-Pressure Industrial Compressors: Hybrid Clustering Models

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dc.contributor.authorCosta, Alessandro
dc.contributor.authorMastriani, Emilio
dc.contributor.authorIncardona, Federico
dc.contributor.authorMunari, Kevin
dc.contributor.authorSpinello, Sebastiano
dc.date.accessioned2024-12-26T21:05:12Z
dc.date.available2024-12-26T21:05:12Z
dc.date.issued2025-01-07
dc.description.abstractThis innovative approach presents a predictive maintenance strategy for high-pressure industrial compressors based on sensor data. In the context of accurately classifying potential compressor failures, this study investigates whether and how much features from upstream unsupervised clustering enhance clustering models in terms of classification accuracy and training performance. The methodology integrates time series analysis, advanced clustering techniques, and hybrid clustering modeling, including feature engineering using auto-correlation analysis and ANOVA, time-series-aware clustering, and six clustering models (Logistic Regression, SVC, GaussianNB, Gradient Boosting, KNC, and RFC). This hybrid clustering sets our work apart from traditional solutions. The approach is validated using cross-validation and key metrics such as accuracy tests. The final results indicate that using the top features identified through unsupervised pre-clustering improves the accuracy of the test set in detecting non operating conditions by an average of 4.87%. Additionally, the training time for clustering models is reduced by an average of 22.96%.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2025.118
dc.identifier.isbn978-0-9981331-8-8
dc.identifier.othera4b44ba0-3045-424c-9cb0-8e91261a68ed
dc.identifier.urihttps://hdl.handle.net/10125/108956
dc.relation.ispartofProceedings of the 58th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectArtificial Intelligence-powered Devices and Sensors
dc.subjecthybrid clustering modeling, industrial compressors, innovation, predictive maintenance, time series analysis
dc.titlePredictive Maintenance Study for High-Pressure Industrial Compressors: Hybrid Clustering Models
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage987

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