Self-Repairing classification algorithms for chemical sensor array

SENSORS AND ACTUATORS B-CHEMICAL

卷:297

Article

Magna, Gabriele¹  Di Natale, Corrado²  Martinelli, Eugenio¹ 

[1] Univ Roma Tor Vergata, Dept Elect Engn, Via Politecn 1, I-00133 Rome, Italy

[2] ITMO Univ, Lab Artificial Sensor Syst, Kronverskiy Prospekt 49, St Petersburg 197101, Russia

关键词: Gas sensor array;    Fault tolerance model;    Self-Repairing;    Sensor drift;    Sensor calibration;   

DOI  :  10.1016/j.snb.2019.126721

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free