A Risk Evaluation Framework in System Control Subject to Sensor Degradation and Failure
Open accessAbstract
Sensor degradation and failure often undermine users’ confidence in adopting a new data-driven decision-making model, especially in risk-sensitive scenarios. A risk assessment framework tailored to classification algorithms is introduced to evaluate the decision-making risks arising from sensor degradation and failures in such scenarios. The framework encompasses various steps, including on-site fault-free data collection, sensor failure data collection, fault data generation, simulated data-driven decision-making, risk identification, quantitative risk assessment, and risk prediction. Leveraging this risk assessment framework, users can evaluate the potential risks of decision errors under the current data collection status. Before model adoption, ranking risk sensitivity to sensor data provides a basis for optimizing data collection. During the use of decision algorithms, considering the expected lifespan of sensors enables the prediction of potential risks the system might face, offering comprehensive information for sensor maintenance. This method has been validated through a case study involving an access control.
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Yuan et al. presented a data-driven method for predicting RUL of sensors considering impacts of sensor degradation on measurement uncertainty, employing a Weibull distribution to estimate failure probabilities over time based on historical sensor failure data.
“Yuan et al. (2024) presented a data-driven method for predicting the remaining useful life of sensors, considering the impacts of sensor degradation on measurement uncertainty, by employing statistical models such as the Weibull distribution to estimate failure probabilities over time based on historical sensor failure data.”
From Impact of Sensor Degradation on Measurement Uncertainty in Prognostics and Maintenance Decision-Making: (2025) · cites this paper as Yuan et al., 2024
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Experimental — accuracy not yet measuredYuan et al. (2024) introduced a risk-based PdM method that quantifies the impact of sensor degradation on decision-making risks to optimize maintenance schedules.
“Yuan et al. (2024) introduced a risk-based PdM method that quantifies the impact of sensor degradation on decisionmaking risks to optimize maintenance schedules.”
From Impact of Sensor Degradation on Measurement Uncertainty in Prognostics and Maintenance Decision-Making: (2025) · cites this paper as Yuan et al. (2024)
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Cites
Data Quality Management in the Internet of Things
Zhang, L., Jeong, D., & Lee, S. (2021) · Sensors · doi:10.3390/s21175834 · Cited by 77 · cited 1× · see citing works
“Furthermore, there is a distinction in data quality between the development and application phases of such algorithms [6].”
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Research works printed but not linked: 20
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Cited by
Sorted by citations
Thai, T.-T. N., Do, P., Iung, B., & Gardoni, P. (2025) · International Journal of Prognostics and Health Management · doi:10.36001/ijphm.2025.v16i4.4306 · Cited by 5 · see citing works
- Authors
- Yuan, T., Xu, W., Adjallah, K. H., Wang, H., Liu, L., & Xu, J.
- Affiliations
- Nanjing University of Science and Technology (CN)
- Laboratoire de Conception, Optimisation et Modélisation des Systèmes (FR)
- Corresponding author
- Yuan, Tangxiao
- Wang, Huifen
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2024-02-28
- Volume
- 24
- Issue
- 5
- Pages
- 1550
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC10934754
- OpenAlex
- W4392240108
- Cited by
- 6
- References
- 1
- Citations by year
- 2026: 4 · 2025: 1 · 2024: 1
- FWCI
- 1.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- High-end Foreign Experts Recruitment Plan of China (G2022182015L, KYCX20_0259)
- Postgraduate Research & Practice Innovation Program of Jiangsu Province (G2022182015L, KYCX20_0259)
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Risk and Safety Analysis
- Keywords
- sensor degradation, sensor faults, risk assessment, machine learning classifiers, data-driven decision making, risk prediction, sensor maintenance, access control, system control, data collection optimization
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