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.

Related papers

• MSSIDD: A Benchmark for Multi-Sensor Denoising [55.41612200877861]
  We introduce a new benchmark, the Multi-Sensor SIDD dataset, which is the first raw-domain dataset designed to evaluate the sensor transferability of denoising models. We propose a sensor consistency training framework that enables denoising models to learn the sensor-invariant features.
  arXiv  Detail & Related papers  (2024-11-18T13:32:59Z)
• Spatial-Temporal Graph Attention Fuser for Calibration in IoT Air Pollution Monitoring Systems [8.997596859735516]
  We propose a novel approach to enhance the calibration process by fusing data from sensor arrays. We demonstrate the effectiveness of our approach in significantly improving the calibration accuracy of sensors in IoT air pollution monitoring platforms.
  arXiv  Detail & Related papers  (2023-09-08T12:04:47Z)
• Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition [3.2319909486685354]
  A key problem holding up progress in wearable sensor-based human activity recognition is the unavailability of diverse and labeled training data. We propose an unsupervised statistical feature-guided diffusion model specifically optimized for wearable sensor-based human activity recognition. By conditioning the diffusion model on statistical information such as mean, standard deviation, Z-score, and skewness, we generate diverse and representative synthetic sensor data.
  arXiv  Detail & Related papers  (2023-05-30T15:12:59Z)
• Environmental Sensor Placement with Convolutional Gaussian Neural Processes [65.13973319334625]
  It is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
  arXiv  Detail & Related papers  (2022-11-18T17:25:14Z)
• Sensor Sampling Trade-Offs for Air Quality Monitoring With Low-Cost Sensors [0.1957338076370071]
  We show the impact of the data sampling strategy in the calibration of tropospheric ozone, nitrogen dioxide and nitrogen monoxide low-cost sensors. Specifically, we show how a sampling strategy that minimizes the duty cycle of the sensing subsystem can reduce power consumption while maintaining data quality.
  arXiv  Detail & Related papers  (2021-12-14T11:05:55Z)
• Bayesian Autoencoders for Drift Detection in Industrial Environments [69.93875748095574]
  Autoencoders are unsupervised models which have been used for detecting anomalies in multi-sensor environments. Anomalies can come either from real changes in the environment (real drift) or from faulty sensory devices (virtual drift)
  arXiv  Detail & Related papers  (2021-07-28T10:19:58Z)
• Bandit Quickest Changepoint Detection [55.855465482260165]
  Continuous monitoring of every sensor can be expensive due to resource constraints. We derive an information-theoretic lower bound on the detection delay for a general class of finitely parameterized probability distributions. We propose a computationally efficient online sensing scheme, which seamlessly balances the need for exploration of different sensing options with exploitation of querying informative actions.
  arXiv  Detail & Related papers  (2021-07-22T07:25:35Z)
• Data-based Design of Inferential Sensors for Petrochemical Industry [0.0]
  Inferential (or soft) sensors are used in industry to infer the values of imprecisely and rarely measured (or completely unmeasured) variables from variables measured online. This work is focused on the design of inferential sensors for product composition of an industrial distillation column in two oil refinery units.
  arXiv  Detail & Related papers  (2021-06-25T08:48:50Z)
• Real-time detection of uncalibrated sensors using Neural Networks [62.997667081978825]
  An online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. The solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively.
  arXiv  Detail & Related papers  (2021-02-02T15:44:39Z)
• Data-aided Sensing for Gaussian Process Regression in IoT Systems [48.523643863141466]
  We use data-aided sensing to learn data sets collected from sensors in Internet-of-Things systems. We show that modified multichannel ALOHA with predictions can help improve the performance of Gaussian process regression with data-aided sensing.
  arXiv  Detail & Related papers  (2020-11-23T20:59:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.