A novel WWH problem-based semi-supervised online method for sensor drift compensation in E-nose

Abstract

Sensor drift caused by the aging components and unsuspected environmental factors is an urgent problem to be solved, as it seriously affects the detection performance and service life of electronic nose (E-nose). Existing researches mainly resorted to offline compensation techniques. Nevertheless, due to the dynamic and uncertainty of sensor drift, the offline techniques are not suitable for practical application scenarios. For this reason, the methods of compensating sensor drift online have been attracting more and more attention. To achieve the online compensation, three problems about the prediction model updating should be addressed first: ① When to update the prediction model (When); ② Which samples are used to update the prediction model (Which); ③ How to update the prediction model (How), that is, a WWH-problem. For addressing the three problems, a WWH problem-based semi-supervised online (WWH-SSO) method is proposed in this paper. The proposed WWH-SSO uses the unlabeled samples collected in the work process of E-nose to update the prediction model for realizing the unsupervised and online drift compensation. The sensor drift benchmark dataset collected by A. Vergara is used to verify the effectiveness of proposed method. The experiment results show that the sensor drift can be satisfactorily compensated online.