An ontology-driven bayesian network approach to fault diagnosis and correction in manufacturing
Abstract
Abstract Efficient Fault Diagnosis and Correction (FDC) is crucial for maintaining high availability in manufacturing systems. This paper presents a novel ontology-driven approach for creating Bayesian Networks (BNs) to support decision-making in FDC. An ontology is developed to represent fault knowledge from domain-specific data sources, including Failure Mode, Effects, and Criticality Analysis (FMECA) data. This ontology is used as a template for creating the BN structure. The probability parameters of the BN are identified heuristically via FMECA criticality information, deterministic relationships, and expert knowledge. A case-based evaluation using an assembly line for solenoid valves demonstrates the BN’s accurate FDC prediction performance.
Fields of Study & Subjects
Bayesian Modeling and Causal Inference
AI-based Problem Solving and Planning
Engineering and Test Systems
Publication details
- Journal / source
- The International Journal of Advanced Manufacturing Technology
- Publisher
- Springer Science and Business Media LLC
- Volume
- 143
- Issue
- 5-6
- Pages
- 2521–2544
- Publication date
- 23 February 2026
- Language
- EN