Time series estimation of gas sensor baseline drift using ARMA and Kalman based models. Issue 1 (18th January 2016)

• Record Type: Journal Article
• Authors: Zhang, Lei; Peng, Xiongwei
• Is Part Of: Sensor review. Volume 36:Issue 1(2016)
• Page Start: 34
• Page End: 39
• Publication Date: 2016-01-18
• Subjects: Gas sensors -- Kalman filter -- Time series prediction -- Electronic nose -- ARMA -- Sensor drift; Sensor systems -- Periodicals; Detectors -- Industrial applications -- Periodicals; Engineering instruments -- Periodicals; 681.2
• DOI: 10.1108/SR-05-2015-0073
• Languages: English
• ISSNs: 0260-2288

Abstract

Abstract : Purpose: – The purpose of this paper is to present a novel and simple prediction model of long-term metal oxide semiconductor (MOS) gas sensor baseline, and it brings some new perspectives for sensor drift. MOS gas sensors, which play a very important role in electronic nose (e-nose), constantly change with the fluctuation of environmental temperature and humidity (i.e. drift). Therefore, it is very meaningful to realize the long-term time series estimation of sensor signal for drift compensation. Design/methodology/approach: – In the proposed sensor baseline drift prediction model, auto-regressive moving average (ARMA) and Kalman filter models are used. The basic idea is to build the ARMA and Kalman models on the short-term sensor signal collected in a short period (one month) by an e-nose and aim at realizing the long-term time series prediction in a year using the obtained model. Findings: – Experimental results demonstrate that the proposed approach based on ARMA and Kalman filter is very effective in time series prediction of sensor baseline signal in e-nose. Originality/value: – Though ARMA and Kalman filter are well-known models in signal processing, this paper, at the first time, brings a new perspective for sensor drift prediction problem based on the two typical models.