FBS Model-based Maintenance Record Accumulation for Failure-Cause Inference in Manufacturing Systems

2025-10-13

AI Summary

In fault root-cause tracing for manufacturing systems, ambiguous knowledge base structures and overly short causal chains lead to inaccurate attribution. Method: This paper proposes a diagnostic knowledge ontology construction method integrating design and operational knowledge. It innovatively incorporates the Function-Behavior-Structure (FBS) model into maintenance record accumulation, enabling explicit support of design-phase knowledge for operational reasoning. The resulting ontology features a well-defined system structure representation and extended causal chains, thereby enhancing explainable reasoning for complex faults. Contribution/Results: Experiments demonstrate that the method significantly improves expert-level matching accuracy of candidate root causes and attribution consistency—particularly under sparse-case and terminology-heterogeneous conditions. It establishes a transferable, knowledge-driven paradigm for intelligent fault diagnosis.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Constructing knowledge ontology for manufacturing failure diagnosis
• Developing FBS-based method to accumulate maintenance records
• Improving failure-cause inference accuracy in manufacturing systems

Innovation

• Constructed Diagnostic Knowledge Ontology for failure analysis
• Proposed FBS model-based maintenance record accumulation method
• Leveraged design phase knowledge for maintenance problem solving