利用自编码器评估无监督学习在直升机发动机预测性维护中的可行性
Assesing the Viability of Unsupervised Learning with Autoencoders for Predictive Maintenance in Helicopter Engines
AI总结：
本文比较了监督分类与自编码器在直升机发动机预测性维护中的性能，发现无监督方法在故障数据稀缺时更具优势。
摘要：
直升机发动机的意外故障可能导致严重的运营中断、安全威胁和昂贵的维修费用。为缓解这些风险，本研究比较了两种直升机发动机的预测性维护策略：一种是基于监督分类的流程，另一种是基于自编码器（AEs）的无监督异常检测方法。监督方法依赖于正常和故障行为的标记示例，而无监督方法仅使用健康发动机数据学习正常操作模型，将偏差标记为潜在故障。两种方法都在包含标记直升机发动机遥测快照的真实世界数据集上进行评估。虽然监督模型在有标注故障时表现强劲，但AE在不需要故障标签的情况下实现了有效的检测，使其特别适合故障数据稀缺或不完整的情况。比较突显了准确率、数据可用性和部署可行性之间的实际权衡，并强调了无监督学习在航空航天应用中早期故障检测的潜力。
英文摘要：
Unplanned engine failures in helicopters can lead to severe operational disruptions, safety hazards, and costly repairs. To mitigate these risks, this study compares two predictive maintenance strategies for helicopter engines: a supervised classification pipeline and an unsupervised anomaly detection approach based on autoencoders (AEs). The supervised method relies on labelled examples of both normal and faulty behaviour, while the unsupervised approach learns a model of normal operation using only healthy engine data, flagging deviations as potential faults. Both methods are evaluated on a real-world dataset comprising labelled snapshots of helicopter engine telemetry. While supervised models demonstrate strong performance when annotated failures are available, the AE achieves effective detection without requiring fault labels, making it particularly well suited for settings where failure data are scarce or incomplete. The comparison highlights the practical trade-offs between accuracy, data availability, and deployment feasibility, and underscores the potential of unsupervised learning as a viable solution for early fault detection in aerospace applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
