Discovery of fully efficient fault indicators along a data-based diagnosis process· 基于数据的故障诊断中全效故障指示器的发现
The integration of model-based and data-driven paradigms provides a powerful framework for fault diagnosis by combining the interpretability of analytical redundancy relations, i.e., input-output relations that are used as diagnosis indicators in model-based diagnosis, with the adaptability of learning techniques. DT4X is a recent diagnosis algorithm that uses symbolic regression to generate multivariate relations leveraging some properties of analytical redundancy relations and uses them as split functions in a decision tree. However, its symbolic regression procedure optimizes only the separation between two selected classes at each node, often fragmenting the remaining classes and degrading both interpretability and diagnosis performance. This paper introduces DT4X+, an enhanced version
提出改进的故障诊断算法 DT4X+，提高基于数据的故障诊断性能和可解释性。
- 核心方法
- DT4X+ 通过改进符号回归过程，优化决策树中的多变量关系，以提高对多个类别的分离效果，增强可解释性和诊断准确性。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 现有故障诊断算法 DT4X 在优化过程中仅考虑两个类别的分离，导致其他类别碎片化，降低诊断性能和可解释性。
- 关键实验
- 在多个故障诊断数据集上进行了实验，验证了 DT4X+ 的性能提升和更好的可解释性。
- 主要贡献
- DT4X+ 算法在保持 DT4X 可解释性优势的同时，显著提高了故障诊断的整体性能。
- 意义与局限
- 该算法为基于数据的故障诊断提供了更强大的工具，尤其适用于复杂系统中的故障检测和分类。但其计算复杂度可能高于传统方法。