On-line novelty detection by recursive dynamic principal component analysis and gas sensor arrays under drift conditions

Leakage detection is a common chemical-sensing application. Leakage detection by thresholds on a single sensor signal suffers from important drawbacks when sensors show drift effects or when they are affected by other long-term cross sensitivities. In this paper, we present an adaptive method based on a recursive dynamic principal component analysis (RDPCA) algorithm that models the relationships between the sensors in the array and their past history. In normal conditions, a certain variance distribution characterizes sensor signals, however, in the presence of a new source of variance the PCA decomposition changes drastically. In order to prevent the influence of sensor drift, the model is adaptive, and it is calculated in a recursive manner with minimum computational effort. The behavior of this technique is studied with synthetic and real signals arising by oil vapor leakages in an air compressor. Results clearly demonstrate the efficiency of the proposed method