Multi-level interpretable logic tree analysis: A data-driven approach for hierarchical causality analysis
作者：
Highlights：
• Interpretable logic tree depicts the fault causality structure in a complex system.
• Knowledge discovery in dataset maximizes the tree representability at each level.
• Data-driven model finds the root-causes with a minimum effort of human experts.
• Causality rules quantify the effects of the root-causes on the fault occurrence.
摘要
•Interpretable logic tree depicts the fault causality structure in a complex system.•Knowledge discovery in dataset maximizes the tree representability at each level.•Data-driven model finds the root-causes with a minimum effort of human experts.•Causality rules quantify the effects of the root-causes on the fault occurrence.
论文关键词：Fault diagnosis,Causality analysis,Knowledge discovery in dataset,Fault tree,Complex system
论文评审过程：Received 10 September 2019, Revised 4 April 2021, Accepted 9 April 2021, Available online 16 April 2021, Version of Record 30 April 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115035
