用FBS模型积累维修记录，提升制造系统故障原因推断准确率
FBS Model-based Maintenance Record Accumulation for Failure-Cause Inference in Manufacturing Systems
- 基于功能-行为-结构模型构建诊断知识本体，规范故障知识表达
- 在案例少、术语不一致时，推断结果与专家判断更吻合
- 适合需要长期知识积累的智能制造系统维护场景
在制造系统中，准确定位故障原因是提升生产效率的关键。基于知识的故障原因推断依赖于知识库：(1) 明确结构化目标系统及故障知识，(2) 包含足够长的故障因果链。本文构建了诊断知识本体，并提出一种基于功能-行为-结构（FBS）模型的维修记录累积方法。利用该方法积累的维修记录进行故障原因推断，与专家列出的候选原因集合具有更高一致性，尤其在相关案例少、术语差异大的困难情形下表现更优。未来需开发适配该记录的推理方法，构建用户界面，并在更大更复杂的系统上验证。该方法利用设计阶段对系统的理解，支持运维阶段的知识积累与问题解决，有望成为全工程链知识共享的基础。
原文摘要 · Abstract (English)
In manufacturing systems, identifying the causes of failures is crucial for maintaining and improving production efficiency. In knowledge-based failure-cause inference, it is important that the knowledge base (1) explicitly structures knowledge about the target system and about failures, and (2) contains sufficiently long causal chains of failures. In this study, we constructed Diagnostic Knowledge Ontology and proposed a Function-Behavior-Structure (FBS) model-based maintenance-record accumulation method based on it. Failure-cause inference using the maintenance records accumulated by the proposed method showed better agreement with the set of candidate causes enumerated by experts, especially in difficult cases where the number of related cases is small and the vocabulary used differs. In the future, it will be necessary to develop inference methods tailored to these maintenance records, build a user interface, and carry out validation on larger and more diverse systems. Additionally, this approach leverages the understanding and knowledge of the target in the design phase to support knowledge accumulation and problem solving during the maintenance phase, and it is expected to become a foundation for knowledge sharing across the entire engineering chain in the future.
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