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.
Fault detection based on signal reconstruction with auto-associative extreme learning machines
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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.