无需详细模型，仅靠基础关系和正常数据就能诊断复杂系统故障
Data Driven Diagnosis for Large Cyber-Physical-Systems with Minimal Prior Information
- 用神经网络生成症状，结合子系统间因果关系图进行诊断
- 82%情况下能包含真实故障源，73%场景下显著缩小搜索范围
- 适合缺乏系统模型的工业级复杂系统故障排查
复杂网络物理系统的诊断通常需要详尽的先验知识，如精确系统模型或全面训练数据，但获取这些信息极具挑战。为此，我们提出一种新诊断方法，仅需基本的子系统关系理解及正常运行数据即可工作。该方法结合基于神经网络的症状生成器（采用子系统级异常检测）与新型图诊断算法，利用实践中通常可获得的最小化因果关系信息。在全可控仿真数据集上的实验表明，该方法在82%的情况下将真实因果组件包含于诊断集合中，且在73%的场景中有效缩小了搜索空间。对真实世界Secure Water Treatment数据集的额外测试进一步验证了其在实际场景中的潜力。结果表明，该方法在先验知识有限的大规模复杂网络物理系统中具有重要应用价值。
原文摘要 · Abstract (English)
Diagnostic processes for complex cyber-physical systems often require extensive prior knowledge in the form of detailed system models or comprehensive training data. However, obtaining such information poses a significant challenge. To address this issue, we present a new diagnostic approach that operates with minimal prior knowledge, requiring only a basic understanding of subsystem relationships and data from nominal operations. Our method combines a neural network-based symptom generator, which employs subsystem-level anomaly detection, with a new graph diagnosis algorithm that leverages minimal causal relationship information between subsystems-information that is typically available in practice. Our experiments with fully controllable simulated datasets show that our method includes the true causal component in its diagnosis set for 82 p.c. of all cases while effectively reducing the search space in 73 p.c. of the scenarios. Additional tests on the real-world Secure Water Treatment dataset showcase the approach's potential for practical scenarios. Our results thus highlight our approach's potential for practical applications with large and complex cyber-physical systems where limited prior knowledge is available.
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