Marta Padilla

Discontinuously Operated Metal Oxide Gas Sensors for Flexible Tag Microlab Applications

Micromachined silicon substrates have significantly reduced the heating power consumption of metal oxide (MOX) gas sensors. Specific applications, however, require further reductions far beyond the present state-of-the-art. In this paper, we report on discontinuously operated MOX gas sensors on micromachined heater platforms and show that such sensors allow power consumption levels to be reached which are consistent with Flexible Tag Microlab (FTM) operation. Such FTMs allow gas concentrations to be measured and recorded to reveal the transport history of goods along the logistics chain for later interrogation by a wireless reader.

Fault detection, identification, and reconstruction of faulty chemical gas sensors under drift conditions, using Principal Component Analysis and Multiscale-PCA

Statistical methods like Principal Components Analysis (PCA) or Partial Least Squares (PLS) and multiscale approaches, have been reported to be very useful in the task of fault diagnosis of malfunctioning sensors for several types of faults. In this work, we compare the performance of PCA and Multiscale-PCA on a fault based on a change of sensor sensitivity. This type of fault affects chemical gas sensors and it is one of the effects of the sensor poisoning. These two methods will be applied on a dataset composed by the signals of 17 conductive polymer gas sensors, measuring three analytes at several concentration levels during 10 months. Therefore, additionally to performances comparison, both methods stability along the time will be tested. The comparison between both techniques will be made regarding three aspects; detection, identification of the faulty sensors and correction of faulty sensors response.

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.

Improving drift correction by double projection preprocessing in gas sensor arrays

It is well known that gas chemical sensors are strongly affected by drift. Drift consist on changes in sensors responses along the time, which make that initial statistical models for gas or odor recognition become useless after a period of time of about weeks. Gas sensor arrays based instruments periodically need calibrations that are expensive and laborious. Many different statistical methods have been proposed to extend time between recalibrations. In this work, a simple preprocessing technique based on a double projection is proposed as a prior step to a posterior drift correction algorithm (in this particular case, Direct Orthogonal Signal Correction). This method highly improves the time stability of data in relation with the one obtained by using only such drift correction method. The performance of this technique will be evaluated on a dataset composed by measurements of three analytes by a polymer sensor array along ten months.