Predictive Maintenance Study for High-Pressure Industrial Compressors: Hybrid Clustering Models
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2025-01-07
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987
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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%.
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Artificial Intelligence-powered Devices and Sensors, hybrid clustering modeling, industrial compressors, innovation, predictive maintenance, time series analysis
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10
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Proceedings of the 58th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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