Cited by 7Reliability and Maintenance OptimizationFault Detection and Control SystemsSoftware Reliability and Analysis Research
The DOI matches its Crossref record.
Specification analysis of the deteriorating sensor for required lifetime prognostic performance
Zhang, Jian-Xun, Si, Xiao-Sheng, Du, Dang-Bo, Hu, Chang-Hua·2018·Microelectronics Reliability
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Crossref · 10.1016/j.microrel.2018.04.004
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In the citing paper
“Investigating sensor degradation in prognostics is crucial, as accurate measurements are essential for predicting system health and sensor degradation is a significant source of measurement uncertainties that undermine the accuracy of the prognostics process (Mukhopadhyay et al., 2023;Zhang et al., 2018;Liu et al., 2019).”
From Impact of Sensor Degradation on Measurement Uncertainty in Prognostics and Maintenance Decision-Making: State of the art and challenges (2025) · cites this paper as Zhang et al., 2018
In the citing paper
“Zhang et al. (2018) effectively quantified the influence of sensor measurement errors on RUL prediction in a blast furnace system by modelling system degradation with a Wiener process that incorporated deteriorating sensor measurement errors quantified using Relative Entropy, and estimated failure time using an Inverse Gaussian distribution, numerical results indicate that controlling the”
From Impact of Sensor Degradation on Measurement Uncertainty in Prognostics and Maintenance Decision-Making: State of the art and challenges (2025) · cites this paper as Zhang et al., 2018
In the citing paper
“Currently, measurement errors are often treated as time-independent random variables; however, in reality, sensor errors tend to change over time due to degradation (Zhang et al., 2018).”
From Impact of Sensor Degradation on Measurement Uncertainty in Prognostics and Maintenance Decision-Making: State of the art and challenges (2025) · cites this paper as Zhang et al., 2018
Citations
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Research works printed but not linked: 35
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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 6
Metadata
- Authors
- Zhang, J.-X., Si, X.-S., Du, D.-B., & Hu, C.-H.
- Venue
- Microelectronics Reliability
- Publisher
- Elsevier BV
- Published
- 2018-06
- Volume
- 85
- Pages
- 71-83
- ISSN
- 0026-2714 (ISSN-L)
- OpenAlex
- W2801993623
- Cited by
- 7
- References
- 0
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 1 · 2022: 1 · 2020: 1
- FWCI
- 0.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Microelectronics Reliability (publishedVersion)
- Funders
- National Natural Science Foundation of China (NSFC) (61573365, 61025014, 61773386, 61573366)
- China Association for Science and Technology (CAST) (2016QNRC001)
- 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 › Computer Science › Software › Software Reliability and Analysis Research
- Keywords
- sensor measurement error, lifetime prediction, degradation data, Wiener process degradation model, Kullback-Leibler divergence, distance measures, estimation bias, maintenance strategy, reliability analysis
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