ArticleSensors (Basel, Switzerland)2019
Permissible Area Analyses of Measurement Errors with Required Fault Diagnosability Performance.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Differentially Private Mobile Crowd Sensing Considering Sensing Errors.Sensors (Basel, Switzerland) · 2020Article
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2 authors.
Funding
Abstract
Fault diagnosability is the basis of fault diagnosis. Fault diagnosability evaluation refers to whether there is enough measurable information in the system to support the rapid and reliable detection of a fault. However, due to unavoidable measurement errors in a system, a quantitative evaluation index of system fault diagnosability is inadequate. In order to overcome the adverse effects of measurement errors, improve the accuracy of the quantitative evaluation of fault diagnosability, and improve the safety level of the system, a method for a permissible area analysis of measurement errors for a quantitative evaluation of fault diagnosability is proposed in this paper. Firstly, in order for the residuals obey normal distribution, a design method of the permissible area of measurement errors based on the Kullback-Leibler divergence (KLD) is given. Secondly, two key problems in calculating the KLD are solved by sparse kernel density estimation and the Monte Carlo method. Finally, the feasibility and validity of the method are analyzed through a case study.
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Registered trials
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