An optimal safety assessment model for complex systems considering correlation and redundancy

Abstract Safety assessments are significant because they can identify the potential risks in complex systems and achieve condition-based maintenance with lower expenses. The safety assessment of a complex system requires an efficient model that can handle numerous correlated features with multiple information sources with uncertainties and reflect complicated nonlinear relationships. Therefore, an optimal safety assessment model for complex systems considering correlation and redundancy is proposed in this paper. First, a two-stage feature selection method is proposed in which the sensitivity factor is adopted to select the relevant features and the maximal information coefficient (MIC) is used to analyze the redundancy between features. Second, weak correlation between features is eliminated by discounting a feature's weight based on the correlation factor. Third, an optimized belief rule base (BRB) is utilized to model the complicated nonlinear relationships of complex systems. Moreover, the referential values for attributes in the BRB are expressed in interval form, which is more consistent with reality. Verification experiments are conducted on a comprehensive gearbox platform and for a real-world case. Compared to the other assessment approaches, the new model has superior attributes, such as higher accuracy and stronger robustness.

• Publication year

  2019

• Venue

  International Journal of Approximate Reasoning

• Publication date

  2019-01-01

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1016/J.IJAR.2018.10.004
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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