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.

翻译：直升机发动机的意外故障可能导致严重的运行中断、安全隐患及高昂维修成本。为降低此类风险，本研究比较了两种直升机发动机预测性维护策略：基于监督分类的流程与基于自编码器的无监督异常检测方法。监督方法依赖于正常与故障行为的标记样本，而无监督方法仅使用健康发动机数据学习正常运行模型，并将偏差标记为潜在故障。两种方法均在包含直升机发动机遥测标记快照的真实数据集上进行评估。尽管监督模型在可获得标注故障数据时表现出优越性能，但自编码器无需故障标签即可实现有效检测，这使其特别适用于故障数据稀缺或不完整的场景。对比结果揭示了准确性、数据可用性与部署可行性之间的实际权衡，并凸显了无监督学习作为航空航天领域早期故障检测可行解决方案的潜力。