# Detection of Sensor-To-Sensor Variations using Explainable AI

arXiv: https://arxiv.org/abs/2306.10850 · HTML: https://mlsanity.com/paper/2306.10850/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 10 of 10)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning](https://mlsanity.com/paper/1912.08881/) (2019)
- [Explainable Artificial Intelligence Approaches: A Survey](https://mlsanity.com/paper/2101.09429/) (2021)
- [GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection](https://mlsanity.com/paper/1903.06661/) (2019)
- [Explaining Anomalies Detected by Autoencoders Using SHAP](https://mlsanity.com/paper/1903.02407/) (2019)
- [Utilizing Explainable AI for Quantization and Pruning of Deep Neural Networks](https://mlsanity.com/paper/2008.09072/) (2020)
- [A general-purpose method for applying Explainable AI for Anomaly Detection](https://mlsanity.com/paper/2207.11564/) (2022)
- [Evaluating Tree Explanation Methods for Anomaly Reasoning: A Case Study of SHAP TreeExplainer and TreeInterpreter](https://mlsanity.com/paper/2010.06734/) (2020)

## Abstract (arXiv)

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.