Incipient fault detection and diagnosis based on KullbackLeibler divergence using principal component analysis: Part II
We propose to enhance the fault detection approach based on the KLD modelling with the introduction of the noise. Based on the aforementioned model an estimator of the fault amplitude is developed and validated. The performances of the detection are studied in a noisy environment with the introduction of the Fault to Noise Ratio (FNR). The robustness of the proposed method is evaluated with the computation of the miss-detection and false alarms probabilities. A performed validation of this approach with a simulated AR model is given.
incipient fault detection and diagnosis kullbackleibler divergence fault estimation detection error probabilities