Environmental Variation or Instrumental Drift? A Probabilistic Approach to Gas Sensor Drift Modeling and Evaluation
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917914241662976 |
|---|---|
| author | Yang, Cheng Bohlin, Gustav Oechtering, Tobias |
| author_facet | Yang, Cheng Bohlin, Gustav Oechtering, Tobias |
| contents | Drift is a significant issue that undermines the reliability of gas sensors. This paper introduces a probabilistic model to distinguish between environmental variation and instrumental drift, using low-cost non-dispersive infrared (NDIR) CO2 sensors as a case study. Data from a long-term field experiment is analyzed to evaluate both sensor performance and environmental changes over time. Our approach employs importance sampling to isolate instrumental drift from environmental variation, providing a more accurate assessment of sensor performance. The results show that failing to account for environmental variation can significantly affect the evaluation of sensor drift, leading to improper calibration processes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17488 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Environmental Variation or Instrumental Drift? A Probabilistic Approach to Gas Sensor Drift Modeling and Evaluation Yang, Cheng Bohlin, Gustav Oechtering, Tobias Signal Processing Drift is a significant issue that undermines the reliability of gas sensors. This paper introduces a probabilistic model to distinguish between environmental variation and instrumental drift, using low-cost non-dispersive infrared (NDIR) CO2 sensors as a case study. Data from a long-term field experiment is analyzed to evaluate both sensor performance and environmental changes over time. Our approach employs importance sampling to isolate instrumental drift from environmental variation, providing a more accurate assessment of sensor performance. The results show that failing to account for environmental variation can significantly affect the evaluation of sensor drift, leading to improper calibration processes. |
| title | Environmental Variation or Instrumental Drift? A Probabilistic Approach to Gas Sensor Drift Modeling and Evaluation |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2406.17488 |