Detection of Sensor-To-Sensor Variations using Explainable AI

• URL: http://arxiv.org/abs/2306.10850v1
• Date: Mon, 19 Jun 2023 11:00:54 GMT
• Title: Detection of Sensor-To-Sensor Variations using Explainable AI
• Authors: Sarah Seifi, Sebastian A. Schober, Cecilia Carbonelli, Lorenzo Servadei, Robert Wille
• Abstract summary: chemi-resistive gas sensing devices are plagued by issues of sensor variations during manufacturing. This study proposes a novel approach for detecting sensor-to-sensor variations in sensing devices using the explainable AI (XAI) method of SHapley Additive exPlanations (SHAP) The methodology is tested using artificial and realistic Ozone concentration profiles to train a Gated Recurrent Unit (GRU) model.
• Score: 2.2956649873563952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the growing concern for air quality and its impact on human health, interest in environmental gas monitoring has increased. However, chemi-resistive gas sensing devices are plagued by issues of sensor reproducibility during manufacturing. This study proposes a novel approach for detecting sensor-to-sensor variations in sensing devices using the explainable AI (XAI) method of SHapley Additive exPlanations (SHAP). This is achieved by identifying sensors that contribute the most to environmental gas concentration estimation via machine learning, and measuring the similarity of feature rankings between sensors to flag deviations or outliers. The methodology is tested using artificial and realistic Ozone concentration profiles to train a Gated Recurrent Unit (GRU) model. Two applications were explored in the study: the detection of wrong behaviors of sensors in the train dataset, and the detection of deviations in the test dataset. By training the GRU with the pruned train dataset, we could reduce computational costs while improving the model performance. Overall, the results show that our approach improves the understanding of sensor behavior, successfully detects sensor deviations down to 5-10% from the normal behavior, and leads to more efficient model preparation and calibration. Our method provides a novel solution for identifying deviating sensors, linking inconsistencies in hardware to sensor-to-sensor variations in the manufacturing process on an AI model-level.

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