A new model based on belief rule base and membership function (BRB-MF) for health state prediction in sensor
Open accessAbstract
Health state prediction is an effective way to improve the reliability for sensors. In the process of sensor degradation, it is difficult to obtain more effective monitoring data. And in the classification of health states, how to identify the adjacent state is also a problem. This paper proposed a health state prediction model based on belief rule base (BRB) and membership function (MF), which is called BRB-MF. In the model, BRB can make full use of expert knowledge and poor effective data. In the prediction results of BRB, it may be not completely logical or not entirely appropriate facing adjacent states of sensor. In order to solve the problem, MF is used to continue the analysis of the predicted results of BRB. In the BRB-MF model, the covariance matrix adaptation evolutionary strategies (CMA-ES) optimization algorithm is used to update the model parameters to make up for the uncertainty of expert knowledge. In the end, the brightness sensor of the rail vehicle LED lighting system is taken as a case study. The results show that the BRB-MF model can predict the health state of sensor with a high accuracy and a reasonable state.
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Sensor degradation impacts prognostics by reducing the availability of effective monitoring data and making it difficult to distinguish adjacent health states, leading to inaccurate predictions of health conditions and increased risk of failure in critical systems.
“Sensor degradation impacts prognostics by reducing the availability of effective monitoring data and making it difficult to distinguish adjacent health states, leading to inaccurate predictions of health conditions and increased risk of failure in critical systems (Yin et al., 2022).”
From Impact of Sensor Degradation on Measurement Uncertainty in Prognostics and Maintenance Decision-Making: (2025) · cites this paper as Yin et al., 2022
Yin et al. proposed a BRB model to encode expert knowledge about sensor degradation and failure mechanisms, represented by probabilities of failure at each time step, accommodating uncertainties and inaccuracies caused by sensor degradation, and using fuzzy logic under Membership Functions to predict future health state.
“Yin et al. (2022) proposed a Belief Rule Base (BRB) model to encode expert knowledge about the sensor's degradation and failure mechanisms, represented by probabilities of failure at each time step of data, which accommodate uncertainties and inaccuracies in the data caused by sensor degradation, and subsequently use fuzzy logic under Membership Functions (MF) to predict the sensor's future health”
From Impact of Sensor Degradation on Measurement Uncertainty in Prognostics and Maintenance Decision-Making: (2025) · cites this paper as Yin et al., 2022
Extensive monitoring data and expert knowledge are often required, but effective sensor health data can often be scarce.
“Extensive monitoring data and expert knowledge are often required, but effective sensor health data can often be scarce (Yin et al., 2022).”
From Impact of Sensor Degradation on Measurement Uncertainty in Prognostics and Maintenance Decision-Making: (2025) · cites this paper as Yin et al., 2022
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
- Yin, X., Shi, G., Peng, S., Zhang, B., & Guo, H.
- Affiliations
- Changchun University of Technology (CN)
- Shandong Transport Vocational College (CN)
- Corresponding author
- Zhang, Bangcheng
- Venue
- Advances in Mechanical Engineering
- Publisher
- SAGE Publications
- Published
- 2022-01
- Volume
- 14
- Issue
- 1
- ISSN
- 1687-8132 (ISSN-L)
- 1687-8140
- OpenAlex
- W4210352068
- Cited by
- 6
- References
- 0
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 1 · 2023: 1
- FWCI
- 0.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Advances in Mechanical Engineering (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Project of the Science and Technology Department of Jilin Province of China (20200301038RQ)
- Project of the Science and Technology Department of Jilin Province of Chnaina (20200403036SF)
- National Natural Science Foundation of China (61803044, 61973046)
- 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
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Keywords
- health state prediction, belief rule base, membership functions, sensor degradation, sensor reliability, condition monitoring
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