Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
This 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%.
Recommended Citation
Costa, Alessandro; Mastriani, Emilio; Incardona, Federico; Munari, Kevin; and Spinello, Sebastiano, "Predictive Maintenance Study for High-Pressure Industrial Compressors: Hybrid Clustering Models" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 2.
https://aisel.aisnet.org/hicss-58/da/sensors/2
Predictive Maintenance Study for High-Pressure Industrial Compressors: Hybrid Clustering Models
Hilton Waikoloa Village, Hawaii
This 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%.
https://aisel.aisnet.org/hicss-58/da/sensors/2