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Development of an On-board Failure Diagnostics and Prognostics System for Solid Rocket Booster

机译:固体火箭助推器机载故障诊断和预测系统的开发

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摘要

We develop a case breach model for the on-board fault diagnostics and prognostics system for subscale solid-rocket boosters (SRBs). The model development was motivated by recent ground firing tests, in which a deviation of measured time-traces from the predicted time-series was observed. A modified model takes into account the nozzle ablation, including the effect of roughness of the nozzle surface, the geometry of the fault, and erosion and burning of the walls of the hole in the metal case. The derived low-dimensional performance model (LDPM) of the fault can reproduce the observed time-series data very well. To verify the performance of the LDPM we build a FLUENT model of the case breach fault and demonstrate a good agreement between theoretical predictions based on the analytical solution of the model equations and the results of the FLUENT simulations. We then incorporate the derived LDPM into an inferential Bayesian framework and verify performance of the Bayesian algorithm for the diagnostics and prognostics of the case breach fault. It is shown that the obtained LDPM allows one to track parameters of the SRB during the flight in real time, to diagnose case breach fault, and to predict its values in the future. The application of the method to fault diagnostics and prognostics (FD&P) of other SRB faults modes is discussed.
机译:我们为超小型实心火箭助推器(SRB)的机载故障诊断和预测系统开发了一个案例突破模型。该模型的开发是由最近的地面射击测试推动的,在该测试中,观测到的时间轨迹与预测的时间序列之间存在偏差。修改后的模型考虑了喷嘴烧蚀,包括喷嘴表面的粗糙度,断层的几何形状以及金属外壳中孔壁的腐蚀和燃烧的影响。故障的导出的低维性能模型(LDPM)可以很好地重现观察到的时间序列数据。为了验证LDPM的性能,我们建立了案例突破故障的FLUENT模型,并证明了基于模型方程的解析解的理论预测与FLUENT仿真结果之间的良好一致性。然后,我们将派生的LDPM合并到推断的贝叶斯框架中,并验证贝叶斯算法对案例突破性故障的诊断和预测的性能。结果表明,所获得的LDPM可以实时跟踪飞行过程中SRB的参数,诊断突发事件并预测其未来值。讨论了该方法在其他SRB故障模式的故障诊断和预测(FD&P)中的应用。

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