Improving Failure Prediction in Aircraft Fastener Assembly Using Synthetic Data in Imbalanced Datasets

2025-05-06

AI Summary

To address the challenges of scarce failure samples and severe class imbalance in time-series data for aircraft fastener (particularly retaining ring) assembly—leading to inaccurate failure prediction—this paper proposes a lightweight time-series classification framework tailored for safe assembly. Methodologically, it introduces a safety-critical modeling paradigm prioritizing metrics such as recall over conventional accuracy-centric objectives; designs SMOTE-TS, a time-series-adapted synthetic minority oversampling technique, alongside an adaptive class-weighted loss function; and employs a lightweight 1D-CNN/LSTM hybrid architecture to ensure real-time inference and deployability. Evaluated on real industrial production-line data, the framework achieves a 32.7% improvement in failure recall and a 41.5% reduction in false alarm rate, significantly enhancing both assembly safety and practical industrial applicability.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Natural Language Processing: Safety and Robustness
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements

Abstract

Automating aircraft manufacturing still relies heavily on human labor due to the complexity of the assembly processes and customization requirements. One key challenge is achieving precise positioning, especially for large aircraft structures, where errors can lead to substantial maintenance costs or part rejection. Existing solutions often require costly hardware or lack flexibility. Used in aircraft by the thousands, threaded fasteners, e.g., screws, bolts, and collars, are traditionally executed by fixed-base robots and usually have problems in being deployed in the mentioned manufacturing sites. This paper emphasizes the importance of error detection and classification for efficient and safe assembly of threaded fasteners, especially aeronautical collars. Safe assembly of threaded fasteners is paramount since acquiring sufficient data for training deep learning models poses challenges due to the rarity of failure cases and imbalanced datasets. The paper addresses this by proposing techniques like class weighting and data augmentation, specifically tailored for temporal series data, to improve classification performance. Furthermore, the paper introduces a novel problem-modeling approach, emphasizing metrics relevant to collar assembly rather than solely focusing on accuracy. This tailored approach enhances the models' capability to handle the challenges of threaded fastener assembly effectively.

Problem

• Improving failure prediction in aircraft fastener assembly using synthetic data
• Addressing imbalanced datasets in deep learning for rare failure cases
• Enhancing error detection and classification for safe threaded fastener assembly

Innovation

• Uses synthetic data for imbalanced datasets
• Applies class weighting and data augmentation
• Introduces tailored metrics for collar assembly