Turbofan Engine Sensor Fault Detection and Isolation Benchmark Dataset

Aircraft engine monitoring relies on sensor measurements to assess gas path condition and to distinguish gradual degradation from abrupt performance changes. In practice, however, sensor signals are influenced simultaneously by the engine state, changing operating conditions, and sensor side effects such as step, drift, random outliers, and measurement noise. This makes it difficult to determine whether an observed deviation originates from the engine, the environment, or the sensing system. For the development and fair comparison of sensor fault detection and isolation methods, a benchmark is required that represents these effects in a controlled and labelled manner. Most publicly available turbofan datasets, however, are primarily intended for remaining useful life prediction and do not provide standardised sensor fault cases for reproducible FDI evaluation. To address this gap, the Turbofan Sensor-FDI-Bench is introduced as a synthetic steady state benchmark dataset generated with a physics based turbofan performance model. The benchmark consists of cruise operating point snapshots and provides, for each flight, environmental conditions, an extended sensor package, and gradual multi component performance degradation. Structured sensor faults with controlled onset and severity are superimposed, including step and drift faults as well as stochastic measurement disturbances. The benchmark is organised as a progressive suite of subsets with increasing complexity, covering fixed and variable operating conditions as well as single fault and multi fault diagnosis settings. For each engine unit, clean reference sensor values are released alongside noisy or faulty measurements, enabling supervised denoising and controlled evaluation of sensor fault diagnosis methods. The resulting benchmark provides a reproducible basis for comparing sensor fault detection and isolation methods under degradation and operating variability.

航空发动机监测依赖传感器测量数据，以评估燃气通路状态，并区分性能的渐进退化与突变。然而在实际场景中，传感器信号同时受发动机状态、变化的运行工况，以及阶跃故障、漂移故障、随机异常值与测量噪声等传感器副效应的影响。这使得难以判断观测到的偏差源自发动机、环境还是传感系统。为开展传感器故障检测与隔离（Fault Detection and Isolation, FDI）方法的开发与公平对比，亟需一个能以可控且带标注的方式复现上述影响的基准数据集。然而，现有多数公开可用的涡轮风扇发动机（Turbofan）数据集主要用于剩余使用寿命预测，并未提供可复现FDI评估所需的标准化传感器故障案例。为填补这一研究空白，本研究提出涡轮风扇发动机传感器FDI基准数据集（Turbofan Sensor-FDI-Bench），其为基于物理涡轮风扇性能模型生成的合成稳态基准数据集。该基准数据集包含巡航工况点的瞬时快照数据，为每个飞行工况提供环境参数、扩展传感器组测量值，以及多部件渐进性能退化数据；同时叠加了发生时刻与严重程度可控的结构化传感器故障，包括阶跃故障、漂移故障以及随机测量扰动。该基准数据集以复杂度递增的递进式子集套件形式组织，涵盖固定与可变运行工况，以及单故障、多故障诊断场景。针对每个发动机单元，数据集同步提供干净的参考传感器值与带噪声或故障的测量数据，可用于监督去噪以及传感器故障诊断方法的可控评估。最终生成的基准数据集为在性能退化与运行工况变化条件下对比传感器FDI方法提供了可复现的研究基础。