Abstract
Fault isolation is an important problem allowing to addres faulty solutions appropriately. In this paper we consider using Bayesian methods represented by functional Gaussian Mixture Model to classify time series representing faults to appropriate categories. We use spline representation of time series and perform Markov Chain Monte Carlo computation to estimate probability of class membership. We show results and supplement them with sensitivity analysis with respect to data quality.
This research was funded by AGH’s Research University Excellence Initiative under project “Interpretable methods of process diagnosis using statistics and machine learning” and by Polish National Science Centre project “Process Fault Prediction and Detection” contract no. UMO-2021/41/B/ST7/03851.
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Baranowski, J. (2023). Application of Bayesian Functional Gaussian Mixture Model Classifier for Cable Fault Isolation. In: Kowalczuk, Z. (eds) Intelligent and Safe Computer Systems in Control and Diagnostics. DPS 2022. Lecture Notes in Networks and Systems, vol 545. Springer, Cham. https://doi.org/10.1007/978-3-031-16159-9_21
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DOI: https://doi.org/10.1007/978-3-031-16159-9_21
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