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Robust Nonlinear Transform Belief Rule Base Based on Double Fuzziness Processing
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AI
DOI：10.1002/adts.70411.png)
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Abstract
En 中文
Health assessment is important for ensuring the safety and stability of systems. However, due to the many uncertainty challenges, there are still three problems with the robustness of belief rule base models: first, the fuzziness of expert knowledge affects the reliability of the assessment results; second, the fuzziness of input information increases the uncertainty in input transformations; and third, effective methods to measure the impact of fuzziness on robustness are still lacking. In this paper, a robust nonlinear transform BRB (RN-BRB) health assessment method is proposed to address these issues. First, the influence of expert knowledge ambiguity on the input is mitigated by reference value optimization, which improves the reliability of the results. Second, the nonlinear input transformation is introduced to ensure the flexibility of input transformation, based on which the RN-BRB assessment model is constructed to enhance further the robustness of the health assessment method. In addition, a robustness metric is invoked to realize the measure of model robustness under the nonlinear input transformation method, which helps to identify and improve the weak points of the model. Finally, the validity and superiority of the RN-BRB model are verified through the health assessment case studies.
Keywords:
belief rule base
fuzziness processing
health state assessment
nonlinear input
robustness
Journal
IF:
2.9
Papers:
488
Citations:
3.6K