Turbofan Sensor-FDI-Bench: A Synthetic Dataset for Sensor Fault Detection & Isolation under Degradation and Operating Variability

Abstract

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.