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PHM by Using Multi-Physics System-Level Modeling and Simulation for EMAs of Liquid Rocket Engine

机译：液体火箭发动机EMA的多物理场系统级建模与仿真

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The need for condition-based maintenance to improve reusable launch vehicle readiness, reliability and safety, with affordable maintenance cost and quick turnaround time is recognized. But the problem of detecting faults and predicting failure in the components of reusable rocket engine systems is difficult and complex to solve. Since the number of data samples on the fault or failure status during the actual operation of the rocket engine is very small, it is difficult to adapt a data-drive approach of health management. Furthermore, the failure modes for these systems might transcend electrical, mechanical, and fluid systems. Therefore, one of the key concepts of the approach proposed in this study for fault detection and diagnosis is model-based quantitative assessment that considers system-level interactions in the target system. In this approach, multi-physics system-level modeling and simulation for a target system are conducted by using Modelica, an equation-based, object-oriented modeling language that allows acausal modeling for complex cyber-physical systems. Modelica has an important modeling capability for system-level interactions that involve multi-physics phenomena. One advantage of the model-based health-monitoring approach is that faults and failure modes are traced back to physically meaningful information, which is invaluable for the maintainer. Thus, this model-based approach for condition-based maintenance has the potential to provide reliable early fault detection and diagnosis during post-flight investigation for maintenance decision-making. In this study, multi-physics system-level modeling and simulation for a target system under both normal and abnormal conditions have been conducted based on an understanding of the failure mechanism to obtain prior data sets for fault detection and diagnosis. In this proposed approach, the Dynamic Time Warping (DTW)algorithm was utilized to evaluate dissimilarity between the prior data sets and sensor measurement data obtained during the flight, and hierarchical clustering technique was applied for categorization in failure mode based on dissimilarity of these data. In addition, a trial case study has been conducted on electromechanical actuators (EMAs), an important component of a rocket engine, towards the construction of model-based prognostics and health management (PHM)technologies for reusable liquid rocket engines. Based on the trial results of the model-based approach constructed in this study, the possibility of fault detection and diagnosis was demonstrated for virtual EMAs of a liquid rocket engine.
机译：人们认识到需要进行基于状态的维护，以提高可重复使用的运载火箭的就绪性，可靠性和安全性，并提供可承受的维护成本和快速的周转时间。但是，在可重复使用的火箭发动机系统的组件中检测故障并预测故障的问题是困难且难以解决的。由于在火箭发动机的实际操作期间关于故障或故障状态的数据样本的数量非常小，因此难以采用健康管理的数据驱动方法。此外，这些系统的故障模式可能会超越电气，机械和流体系统。因此，本研究中提出的用于故障检测和诊断的方法的关键概念之一是基于模型的定量评估，该评估考虑了目标系统中系统级的交互作用。在这种方法中，通过使用Modelica（针对基于方程的面向对象的建模语言Modelica）对目标系统进行多物理场系统级建模和仿真，该模型语言可以对复杂的电子物理系统进行因果关系建模。对于涉及多物理现象的系统级交互，Modelica具有重要的建模能力。基于模型的健康监控方法的一个优势是，故障和失败模式可追溯到对身体有意义的信息，这对于维护人员而言是无价的。因此，这种基于模型的状态维护方法具有潜力，可以在飞行后调查期间为维护决策提供可靠的早期故障检测和诊断。在这项研究中，基于对故障机理的了解，为目标系统在正常和异常条件下进行了多物理场系统级建模和仿真，以获取用于故障检测和诊断的先前数据集。在该提议的方法中，动态时间规整（DTW）算法用于评估飞行过程中获得的先验数据集与传感器测量数据之间的不相似性，并且基于这些数据的不相似性，将分层聚类技术应用于故障模式下的分类。此外，已经针对火箭发动机的重要组成部分机电致动器（EMA）进行了试验案例研究，旨在为可重复使用的液体火箭发动机构建基于模型的预测和健康管理（PHM）技术。根据这项研究中基于模型的方法的试验结果，证明了液体火箭发动机虚拟EMA的故障检测和诊断的可能性。

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