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Journal article
AI-Driven Failure Analysis in Engineering Systems: : LSTM-Based Failure Prediction with Efficient Dependency-Graph Transitive-Closure Updates
Journal of Research in Engineering and Computer Sciences, Vol.4(5), pp.55-74
09/16/2026
Abstract
Failure analysis is essential for ensuring the reliability and safety of engineering systems. However, conventional approaches are often too resource-intensive to keep pace with the growing complexity and interconnectivity of modern engineering systems. This research proposes a novel, AI-driven combinatorial approach to failure analysis. By integrating natural language processing (NLP), machine learning (ML), and graph-theoretic methods, the approach aims to improve component-failure prediction, generate actionable insights, and help safety and reliability engineers identify failure patterns that inform more resilient system designs. The proposed approach uses a long short-term memory (LSTM) network to predict component failure probabilities and represents complex interdependencies among system components with a dependency graph. The graph’s transitive closure is cached and incrementally maintained using efficient algorithms, enabling on-demand recalculation of estimated component failure probabilities as the system design changes. An aerospace engineering case study is used to demonstrate the approach’s application in a real-world setting. The proposed AI-driven approach offers a promising path to improving system safety and reliability across a range of engineering domains, particularly in industries that operate highly complex, safety-critical systems. Future work should train the LSTM on larger, more representative datasets to improve the accuracy of failure-probability predictions.
Details
- Title
- AI-Driven Failure Analysis in Engineering Systems:
- Publication Details
- Journal of Research in Engineering and Computer Sciences, Vol.4(5), pp.55-74
- Resource Type
- Journal article
- Publisher
- HS Publishing
- Copyright
- © the author(s)
- Identifiers
- 99381907822506600
- Academic Unit
- Mathematics and Statistics; Hal Marcus College of Science and Engineering
- Language
- English