Computationally predicting the performance of gas sensor arrays for anomaly detection†

Abstract

In many gas sensing tasks, we simply wish to become aware of gas compositions that deviate from normal, “business-as-usual” conditions. We provide a methodology, illustrated by example, to computationally predict the performance of a gas sensor array design for detecting anomalous gas compositions. Specifically, we consider a sensor array of two zeolitic imidazolate frameworks (ZIFs) as gravimetric sensing elements for detecting anomalous gas compositions in a fruit ripening room. First, we define the probability distribution of the concentrations of the key gas species (CO₂, C₂H₄, H₂O) we expect to encounter under normal conditions. Next, we construct a thermodynamic model to predict gas adsorption in the ZIF sensing elements in response to these gas compositions. Then, we generate a synthetic training data set of sensor array responses to “normal” gas compositions. Finally, we train a support vector data description to flag anomalous sensor array responses and test its false alarm and missed-anomaly rates under conceived anomalies. We find the performance of the anomaly detector diminishes with (i) greater variance in humidity, which can mask CO₂ and C₂H₄ anomalies or cause false alarms, (ii) higher levels of noise emanating from the transducers, and (iii) smaller training data sets. Our exploratory study is a step towards computational design of gas sensor arrays for anomaly detection.