  Chemical sensors are usually affected by drift, have low fabrication
reproducibility and can experience failure or breaking events over the long
term. Albeit improvements in fabrication processes are often slow and
inadequate for completely surmounting these issues, data analysis can be used
as of now to improve the available device performances. The present paper
illustrates an algorithm, called Self-Repairing (SR), developed for repairing
classification models after the occurrences of failures in sensor arrays. The
procedure considers replacing broken sensors with replicas and eventually
Self-Repairing algorithm trains these blank elements. Unlike the habitual
alternatives reported in literature, SR performs this operation without the
need of a whole new recalibration, references gas measurements or transfer
dataset and, at the same time, without interrupting the on-going procedure of
gas identification. Furthermore, Self-Repairing algorithm can utilize most of
the standard classifiers as core algorithm; in this paper SR has been applied
to k-NN, PLS-DA and LDA as examples. Models have been tested in a synthetic and
real scenario considering sensor arrays affected by drift and eventually by
failures. Real experiment has been performed with a set of metal oxide sensors
over an 18-months period. Finally, the algorithm has been compared with
standard version of chosen classifiers (k-NN, LDA and PLS-DA) showing superior
performances of Self-Repairing and increasing the tolerance versus consecutive
failures.
