Capability evaluation of incipient fault detection in noisy environment: A theoretical Kullback-Leibler Divergence-based approach for diagnosis

Process-history based methods are very commonly used for fault diagnosis and detection. However their efficiency is closely related to the quality of the measured data. In noisy environments, they usually fail particularly for incipient faults. This paper is an attempt to determine an analytical model allowing to estimate a theoretical threshold for fault detection based on the Fault to Noise Ratio (FNR). This model is developed using the Kullback-Leibler Divergence (KLD). For feature extraction, the used data are previously processed through Principal Component Analysis (PCA). The model is validated with simulated data and the results are so far very encouraging.