IEEE Trans Cybern. 2022 Jun;52(6):4300-4311. doi: 10.1109/TCYB.2020.3025800. Epub 2022 Jun 16.
Fault diagnosis plays a critical role in maintaining and troubleshooting engineered systems. Various diagnosis models, such as Bayesian networks (BNs), have been proposed to deal with this kind of problem in the past. However, the diagnosis results may not be reliable if second-order uncertainty is involved. This article proposes a hierarchical system diagnosis fusion framework that considers the uncertainty based on a belief model, called subjective logic (SL), which explicitly deals with uncertainty representing a lack of evidence. The proposed system diagnosis fusion framework consists of three steps: 1) individual subjective BNs (SBNs) are designed to represent the knowledge architectures of individual experts; 2) experts are clustered as expert groups according to their similarity; and 3) after inferring expert opinions from respective SBNs, the one opinion fusion method was used to combine all opinions to reach a consensus based on the aggregated opinion for system diagnosis. Via extensive simulation experiments, we show that the proposed fusion framework, consisting of two operators, outperforms the state-of-the-art fusion operator counterparts and has stable performance under various scenarios. Our proposed fusion framework is promising for advancing state-of-the-art fault diagnosis of complex engineered systems.
故障诊断在维护和故障排除工程系统中起着至关重要的作用。过去已经提出了各种诊断模型，如贝叶斯网络（BNs），以解决此类问题。然而，如果涉及二阶不确定性，诊断结果可能不可靠。本文提出了一种基于置信模型的分层系统诊断融合框架，称为主观逻辑（SL），它明确处理不确定性，代表缺乏证据。所提出的系统诊断融合框架由三个步骤组成：1）设计单个主观贝叶斯网络（SBN）来表示单个专家的知识体系；2）根据相似性将专家聚类为专家组；3）从各自的 SBN 中推断专家意见后，使用一种意见融合方法将所有意见结合起来，根据聚合意见达成系统诊断共识。通过广泛的仿真实验，我们表明，由两个运算符组成的所提出的融合框架优于最先进的融合运算符对应物，并且在各种场景下具有稳定的性能。我们提出的融合框架有望推进复杂工程系统的故障诊断的最新技术。
