A hybrid framework that feeds calibrated model parameters and virtual sensors into deep one-class classifiers achieves near-perfect fault detection and isolation on a synthetic turbofan dataset, but the isolation result relies on including the fault-generating parameter in the input.
Power distribution fault cause identification with imbalanced data using the data mining-based fuzzy classificatione-algorithm
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Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models
A hybrid framework that feeds calibrated model parameters and virtual sensors into deep one-class classifiers achieves near-perfect fault detection and isolation on a synthetic turbofan dataset, but the isolation result relies on including the fault-generating parameter in the input.