RESEARCH PAPER
A Data-Driven Complex Network Approach for Aviation Piston Engine Safety: Critical Causation Analysis and Predictive Intervention
1
Beihang University, China
2
Tianmushan Laboratory, China
3
Civil Aviation University of China, China
These authors had equal contribution to this work
Submission date: 2025-08-22
Final revision date: 2025-11-06
Acceptance date: 2026-02-19
Online publication date: 2026-03-01
KEYWORDS
TOPICS
ABSTRACT
Aviation piston engines (APEs), the primary propulsion systems in general aviation, are critical to flight safety. To understand the accident causation mechanism of APEs, this study proposes a data-driven methodology that integrates complex network (CN) theory with a three-step criticality analysis (TSCA). Maintenance records provide the data source for causal chain extraction and CN construction. In TSCA process, topology analysis using ICW-TOPSIS quantitatively identifies critical risk factors. Considering the uniqueness of component factors in physical systems, a hybrid metric integrating both objective and subjective dimensions is designed for accurate component evaluation. For critical risk path analysis, we develop an efficient path searching algorithm initiating from these prioritized components. The CN-TSCA findings enable the formulation of multi-phase safety control strategies. Leveraging historical maintenance records, this method effectively identifies risks in complex physical systems and provides a systematic framework for safety enhancement and preventive strategies.
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| ISSN: | 1507-2711 |
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