A Probabilistic Model for Information and Sensor Validation
Open accessAbstract
This paper develops a new theory and model for information and sensor validation. The model represents relationships between variables using Bayesian networks and utilizes probabilistic propagation to estimate the expected values of variables. If the estimated value of a variable differs from the actual value, an apparent fault is detected. The fault is only apparent since it may be that the estimated value is itself based on faulty data. The theory extends our understanding of when it is possible to isolate real faults from potential faults and supports the development of an algorithm that is capable of isolating real faults without deferring the problem to the use of expert provided domain-specific rules. To enable practical adoption for real-time processes, an any time version of the algorithm is developed, that, unlike most other algorithms, is capable of returning improving assessments of the validity of the sensors as it accumulates more evidence with time. The developed model is tested by applying it to the validation of temperature sensors during the start-up phase of a gas turbine when conditions are not stable; a problem that is known to be challenging. The paper concludes with a discussion of the practical applicability and scalability of the model.
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Sensor validation is used to evaluate the authenticity of a sensor system and adjust or correct it as needed.
“Sensor validation is used to evaluate the authenticity of a sensor system and adjust or correct it as needed [48].”
From Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey (2022) · cites this paper as [48]
Section 2 defines validation as determining whether sensor readings are valid, and Section 3 describes identifying faulty readings. The paper does not say that validation adjusts or corrects the sensor system; its focus is assessing readings and isolating faults. Page numbers are not available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredSeveral other techniques, such as sensor validation, can improve sensor reliability.
“Several other techniques, such as sensor validation, can improve sensor reliability.”
From Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey (2022) · cites this paper as [48]
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Cited by
Sorted by citations
Sensor Systems for Prognostics and Health Management
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- Authors
- Ibargüengoytia, P. H., Vadera, S., & Sucar, L. E.
- Affiliations
- Instituto de Investigaciones Eléctricas (MX)
- University of Salford (GB)
- Tecnológico de Monterrey (MX)
- Corresponding author
- Ibargüengoytia, Pablo H.
- Venue
- The Computer Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-10-24
- Volume
- 49
- Issue
- 1
- Pages
- 113-126
- ISSN
- 0010-4620 (ISSN-L)
- 1460-2067
- OpenAlex
- W2009426271
- Cited by
- 43
- References
- 51
- Citations by year
- 2025: 1 · 2022: 1 · 2020: 1 · 2019: 2 · 2018: 3 · 2016: 3 · 2015: 2 · 2014: 2 · 2013: 3 · 2012: 5
- FWCI
- 5.01
- Type
- journal article
- Language
- en
- Open access
- green
- usir.salford.ac.uk
- Licence
- cc-by
- Locations
- The Computer Journal (publishedVersion)
- University of Salford Institutional Repository (University of Salford) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Engineering and Test Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- sensor validation, data validation, Bayesian networks, fault detection, fault isolation, sensor fault diagnosis, anytime algorithms, real-time processes, temperature sensors, gas turbine start-up
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