Poisoning fault diagnosis in chemical gas sensor arrays using multivariate statistical signal processing and structured residuals generation

Chemical gas sensors are a cheaper and faster alternative for gas analysis than conventional analytic instruments. .However they are prone to degradation because of sensor poisoning and drift. Statistical methods like principal component analysis (PCA) and partial least squares (PLS) have been proved to be very useful in the task of fault diagnosis of malfunctioning sensors. In this work we test the effectiveness of several techniques based on PCA and PLS on faults caused by sensor poisoning These techniques will be evaluated on a dataset composed by the signals of 17 conductive polymers gas sensors measuring three analytes at several concentration levels. These techniques will be evaluated concerning their capabilities to detect the fault, identify the faulty sensor and correct their signal.