To avoid infrared or thermal signatures of the fixed-wing unmanned aerial vehicle, the engine is encapsulated in a special cowling that limits the ventilation and causes thermal stress. The stressed condition heats up the engine and accelerates the degradation process compromising life and causing early failure. Fatigue life estimation can help to predict and prevent sudden failure and improve safety and reliability. The study presents a grey forecasting methodology for estimating the fatigue life of fixed-wing unmanned aerial vehicle engines operating under a stressed environment. Grey forecasting models are used for fatigue life estimation of the unmanned aerial vehicle engine using degradation data of output power for reliable flight hours (50 h). The result of grey forecasting models reveals that under normal operation, engine power drops to a threshold value of 9.4 kW (below this engine does not remain flight worthy) after 100 h. The forecasted life is in close agreement with the specification of the engine under normal operating conditions. This validates the accuracy of forecasting models. Furthermore, the forecast models are applied to estimate the fatigue life using degradation data in a stressed environment, which comes out to be 70 h. The study proposes application of grey forecasting to predict mechanical degradation and early failures by considering single or multiple parameters undergoing degradation and having limited data samples. Forecasting results are compared with other prediction tools like autoregressive–moving-average and found more accurate which shows the significance of grey forecasting models in a limited data sample environment. The results are also compared with exponential regression and found in close agreement but more robust.

为了避免固定翼无人机的红外或热信号，发动机被封装在特殊的整流罩中，该整流罩限制了通风并引起热应力。压力条件会加热发动机并加速降解过程，从而损害使用寿命并导致早期故障。疲劳寿命估算可以帮助预测和防止突然失效，并提高安全性和可靠性。该研究提出了一种灰色预测方法，用于估计在压力环境下运行的固定翼无人机飞机发动机的疲劳寿命。灰色预测模型用于通过可靠飞行小时（50小时）的输出功率退化数据估算无人机发动机的疲劳寿命。灰色预测模型的结果表明，在正常操作下，100小时后，发动机功率下降到9.4 kW的阈值（低于该发动机无法保持飞行状态）。在正常工作条件下，预计寿命与发动机的规格密切相关。这验证了预测模型的准确性。此外，将预测模型应用于使用应力环境下​​的退化数据（估计为70小时）来估计疲劳寿命。这项研究提出了灰色预测的应用，通过考虑经历退化和数据样本有限的单个或多个参数来预测机械退化和早期故障。将预测结果与其他预测工具（例如自回归移动平均）进行比较，发现更准确，这表明了在有限的数据样本环境中灰色预测模型的重要性。