NLP and Association Rules for Intelligent Fault Analysis
Wang, Z., et al.
Journal of Intelligent Manufacturing (2023)
Abstract
This paper presents an innovative approach to intelligent fault analysis using
natural language processing techniques combined with association rules. Our
methodology extracts meaningful patterns from technical documentation and
failure reports to enable automated fault detection and analysis.
Key Contributions
Advanced NLP techniques for processing technical documentation
Association rule mining for pattern discovery in failure data
Intelligent fault classification and categorization
Automated extraction of failure patterns from unstructured text
Real-time fault analysis and prediction capabilities
Key Findings
Achieved 87% accuracy in fault pattern recognition
Reduced manual analysis time by 70% through automation
Improved fault classification accuracy by 25%
Enhanced ability to process multi-language technical documents
Significant improvement in early fault detection
Methodology
Our approach combines state-of-the-art natural language processing with
association rule mining to extract meaningful patterns from technical
documentation. The system processes unstructured text data to identify
fault patterns and generate actionable insights for failure analysis.