Preemptive Failure Detection using Contamination-Based Stacking Ensemble in Missiles

• Seong-Mok Kim,
• Ye-Eun Jeong,
• Yong Soo Kim,
• Youn-Ho Lee,
• Seung Young Lee,

Vol. 18, No. 5, May 31, 2024
10.3837/tiis.2024.05.009, Download Paper (Free):

• Weapon System
• Missile
• Quality Control
• Anomaly Detection
• Unsupervised learning

Abstract

In modern warfare, missiles play a pivotal role but typically spend the majority of their lifecycle in long-term storage or standby mode, making it difficult to detect failures. Preemptive detection of missiles that will fail is crucial to preventing severe consequences, including safety hazards and mission failures. This study proposes a contamination-based stacking ensemble model, employing the local outlier factor (LOF), to detect such missiles. The proposed model creates multiple base LOF models with different contamination values and combines their anomaly scores to achieve a robust anomaly detection. A comparative performance analysis was conducted between the proposed model and the traditional single LOF model, using production-related inspection data from missiles deployed in the military. The experimental results showed that, with the contamination parameter set to 0.1, the proposed model exhibited an increase of approximately 22 percentage points in accuracy and 71 percentage points in F1-score compared to the single LOF model. This approach enables the preemptive identification of potential failures, undetectable through traditional statistical quality control methods. Consequently, it contributes to lower missile failure rates in real battlefield scenarios, leading to significant time and cost savings in the military industry.

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