【作者】 孙见忠；

【导师】 左洪福；

【作者基本信息】 南京航空航天大学 ， 载运工具运用工程， 2012， 博士

【摘要】 随着我国民航机队规模的不断扩大，民航安全形势和压力越来越严峻，新一代航空运输系统也对行业提出了“安全关口前移”和“持续安全”的发展目标，民航发动机的安全和成本效益问题是亟待研究的重大问题。国内航空产业领域，新一代国产军用和民用发动机均对发动机健康管理（EHM）提出了需求。因此，无论从我国民航业工程应用需要，还是从国产发动机EHM系统的研究开发的角度出发，亟需在EHM基础理论、关键技术等方面开展相关研究。本文在充分研究了EHM概念和技术架构、理清了关键技术的发展现状及趋势的基础上，结合国内在发动机健康管理工程实践中存在的问题与不足，以气路部件为对象，分别在气路部件状态监控、单元体健康状态评估及剩余寿命预测建模三个方面展开了深入研究，一方面可为提高我国民航发动机的健康管理水平提供可行的方法和技术，另一方面也可借鉴民航发动机健康管理领域成熟的技术和方法为国产发动机EHM系统的开发提供技术储备。论文主要工作如下：（1）气路部件状态监控方面：在挖掘现有的气路监控技术的潜力方面，针对性能参数偏差值离散度大、故障特征容易被噪声淹没的问题，提出了基于贝叶斯因子的性能参数偏差值序列监控方法，以从含有噪声的偏差值序列中及时识别异常，提前故障征兆发现的时机；针对“一般”基线模型难以准确反映实际运行环境下个体发动机的特性的问题，提出了基于多元状态估计技术的“个性化”基线模型的挖掘方法，依据发动机实测数据建立起的“个性化”基线模型能更准确的特定发动机的特性，因此，能够更准确地计算出性能参数的偏差量，有利于提前故障征兆的发现时机。在新型气路监测技术方面，把尾气静电信号作为一种新的气路状态参数，提出了基于燃油流量单参数的和基于多元状态估计技术的多参数尾气静电信号基线模型挖掘方法。在建立起尾气静电信号的基线模型后，通过实时监控尾气静电信号RMS值与基线值的偏差，实现对气路部件状态的实时监控，提前故障征兆的发现时机。（2）面向单元体的气路部件健康状态评估方面：研究了航线条件下基于非线性自适应性能模型的单元体健康状态评估方法。考虑到外场条件下可测气路参数少于待估计的健康参数以及测量噪声的影响，将复杂的健康参数的求解问题转换为一个寻优问题，提出了基于排除法的故障隔离—评估方法，通过排除健康单元体把待估计参数维数降低到低于可测气路参数；针对实际外场使用中各单元体不可避免的发生缓慢的性能退化的问题，提出了考虑性能缓慢退化情况下的单元体健康参数的估计方法，首先通过跟踪各单元体的缓慢性能退化，得到故障前各单元体的实际退化状态，进而在故障隔离与评估时考虑进其影响以提高单元体健康参数估计的精度。针对气路可测参数有限、信息源单一的问题，提出了基于贝叶斯网络多源诊断信息融合的气路分析技术：以常规气路可测参数为主，定性诊断信息借助故障模式先验概率表引进，而定量信息的引入则借助健康参数的先验分布实现，由贝叶斯网络实现信息融合，提高单元体健康参数估计的准确度和精度。（3）健康状态与剩余寿命预测建模方法研究：针对实际运行环境下个体系统的剩余寿命预测问题，提出了基于状态空间退化模型和贝叶斯估计理论的使用可靠性评估与剩余寿命预测方法。以发动机排气温度裕度作为表征气路部件性能衰退的退化参数，由线性高斯状态空间模型来描述性能退化轨迹，利用共轭先验贝叶斯推理估计并预测退化状态。状态空间模型区分带有噪声的观测量与系统真实的退化状态，更加符合实际情况。此外，状态空间模型不需要对退化轨迹做平稳性假设，这使得模型便于处理因维修、故障等因素而引起的性能突变。考虑到仅用单参数难以全面地表征系统健康状况的问题，提出了通过融合多个性能参数得到系统的健康指数来表征气路部件健康状态，进而建立基于系统健康指数的状态空间退化模型，用于预测其健康状态退化趋势及剩余寿命。针对关键件的裂纹扩展这一典型的失效模式，提出了物理失效模型和检测/监测数据相融合的剩余寿命预测方法，根据其物理失效模型建立起状态空间形式的裂纹扩展模型，借助贝叶斯理论融合外场检测/监测信息，通过不断融合新的观测信息可降低剩余寿命预测的不确定性，为关键件延寿或视情维修提供辅助决策。更多还原

【Abstract】 With the rapid development of civil aviation industry, the civil aircraft engine safety andbenefit/cost issue become an urgent problem. The next-generation air transportation system isproposed with the targets of “checkpoints shifted front for safety” and “continued safety”. Thedomestically produced civil and military aero engine has a need on the Engine Health Management(EHM) technologies. So there is an urgent need to strengthen the research on the key technologies toimprove the EHM capability of the civil aircraft engines and to support the development anddeployment of the EHM system for the domestically produced aero engines. Based on the literaturesurvey and a full study of the concept and technical architecture of the EHM, considering theproblems in the practice, the main topics of the thesis focus on the gas path components conditionmonitoring, health status assessment as well as remaing useful life prognostics methods. On one sideit can provide technologies and methods for the improvement of the civil aircraft engine EHMcapability, on the other side it can provide experiences and mature technologies for the domesticallyproduced engine’s EHM system. The main topics of the thesis are as follows:(1) Gas path components condition monitoring:In order to detect the anomaly as soon as possible from the delta parameter parameters with largenoise and scatters, which maybe cover the fault signature, then to trigger a timely warning at early stageof the fault, a Bayesian factor-based method is proposed to monitor and analyze parameter delta series.Since the “generic” baseline model embedded in the OEM software cannot capture the characteristicsof the individual engine under the real operating conditions, the Multi State Estimation Technique(MSET) is proposed to build the individual baseline model for the specific engine, which can capturethe characteristics of the individual engine. Based on the individual baseline model, more accurate deltadata can be obtained which can advance the fault warning in time. In the application of the new gaspath condition monitoring techniques, the Exhaust Gas Electrostatic Monitoring Signal (EGEMS) isthought as a new gas path performance parameter, and two EGEMS baseline model building methodsare proposed. One is based on one parameter-the fuel flow rate, and the other one is based on multiparameters which are correlated using the MSET. Based on the developed baseline model, the deltabetween the real RMS value of the EGEMS and baseline value is monitored in real time to monitor thegas path component condition and to trigger a warning once some fault occures.(2) Gas path components health assessment:A study of in-field engine components health assessment techniques based on adaptive engineperformance model is carried out. Considering the fact that less measured performance parameters thanthe health parameters to estimate and the influence of the measurement noise, the health parametersestimation problem is changed into an optimization problem. An exclusive method based faultdetection and assessment framework is proposed, in which the healthy module is excluded one-by-one to reduce the number of the health parameters to estimate until they are less than the measuredparameters. Considering the fact that the gradual performance deterioration in field is inevitable, ahealth parameter estimation framework is proposed to incorporate the gradual performancedeterioration information. In this framework, the gradual performance deterioration is tracked to get thedegradation state of each module before the fault, then the information is incorporated when isolatingand assessment the fault to improve the health assessment results. A information fusion based gas pathanalysis framework is proposed to tackle the issue of lack of enough measured gas path parameters. Inthis framework, an information fusion mechanism based on the Bayesian network is developed toincorporate the the diagnosis information from multi sources, in which the qualitative information isincorporated by the fault mode prior probability table and the quantitative information is incorporatedby the prior distribution of the health parameter, then the information is fused using Bayesian rules toimprove the accuracy and precision of the estimation results of the health parameters.(3) Modeling methods for remaining useful life prognostics:The methods on remaining useful life prognosis for individual system under real operatingconditions are discussed in depth. The state space-based degradation model combined with Bayesianstate estimation theory is proposed for system remaining useful life prognostics and in-servicereliability estimation. The EGTM parameter is used as a degradation parameter to quantify thedegradation state of the engine. Then linear Gaussian state space model is adapted to describe thedegradation trajectory based on the observed EGTM data, and then the conjugate Bayesian inferenceis carried out to estimate the degradation state and further to make a prediction of the failure time. Thestate space based degradation model differentiates the noisy observation from the true degradationstate, which is closer to the actual case. The state space degradation model does not need to makestationarity assumption, so it can effectively manage the situation when there is a sudden change inthe health state due to fault or maintenance. Considering the problems that a single parameter cannotcharacterize the health state of a complex system, a fusion mechanism is developed to fuse multiparameters to get a health index to characterize the health state of the gas path component, based onwhich a state space degradation model is established to describe the degradation path and predict theremaining useful life. For the crack growth failure of the critical components, a fusion framework isproposed to integrate the damage monitoring data and physics of failure mechanism for remaininguseful life prediction. A state space based crack growth model is developed based the Paris crackgrowth model, then the damage monitoring data is integrated using the Bayesian rule. By integratingthe monitoring data the prognostics uncertainty can be continued reduced.更多还原