Multi-mode diagnosis of a gas turbine engine using an adaptive neuro-fuzzy system

Gas Turbine Engines (GTEs) are vastly used for generation of mechanical power in a wide range of applications from airplane propulsion systems to stationary power plants. The gas-path components of a GTE are exposed to harsh operating and ambient conditions, leading to several degradation mechanisms. Because GTE components are mostly inaccessible for direct measurements and their degradation levels must be inferred from the measurements of accessible parameters, it is a challenge to acquire reliable information on the degradation conditions of the parts in different fault modes. In this work, a data-driven fault detection and degradation estimation scheme is developed for GTE diagnostics based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). To verify the performance and accuracy of the developed diagnostic framework on GTE data, an ensemble of measurable gas path parameters has been generated by a high-fidelity GTE model under (a) diverse ambient conditions and control settings, (b) every possible combination of degradation symptoms, and (c) a broad range of signal to noise ratios. The results prove the competency of the developed framework in fault diagnostics and reveal the sensitivity of diagnostic results to measurement noise for different degradation symptoms.     

Plastic wrinkling prediction in thin-walled part forming process:A review

The precision forming of thin-walled components has been urgently needed in aviation and aerospace field. However, the wrinkling induced by the compressive instability is one of the major defects in thin-walled part forming. The initiation and growth of the wrinkles are interac-tively affected by many factors such as stress states, mechanical properties of the material, geometry of the workpiece and boundary conditions. Especially when the forming process involves compli-cated boundary conditions such as multi-dies constrains, the perturbation of clearances between workpiece and dies and the contact conditions changing in time and space, etc., the predication of the wrinkling is further complicated. In this paper, the current prediction methods were summa-rized including the static equilibrium method, the energy method, the initial imperfection method, the eigenvalue buckling analysis method, the static-implicit finite element method and the dynamic-explicit finite element method. Then, a systematical comparison and summary of these methods in terms of their advantages and limitations are presented. By using a combination of explicit FE method, initial imperfection and energy conservation, a hybrid method is recommended to predict plastic wrinkling in thin-walled part forming. Finally, considering the urgent requirements of com-plex thin-walled structures’ part in aviation and aerospace field, the trends and challenges in wrin-kling prediction under complicated boundary conditions are presented.     

DME Interference mitigation for L-DACS1 based on system identification and sparse representation

L-band digital aeronautical communication system 1 (L-DACS1) is a promising candi-date data-link for future air-ground communication, but it is severely interfered by the pulse pairs (PPs) generated by distance measure equipment. A novel PP mitigation approach is proposed in this paper. Firstly, a deformed PP detection (DPPD) method that combines a filter bank, correlation detection, and rescanning is proposed to detect the deformed PPs (DPPs) which are caused by mul-tiple filters in the receiver. Secondly, a finite impulse response (FIR) model is used to approximate the overall characteristic of filters, and then the waveform of DPP can be acquired by the original waveform of PP and the FIR model. Finally, sparse representation is used to estimate the position and amplitude of each DPP, and then reconstruct each DPP. The reconstructed DPPs will be sub-tracted from the contaminated signal to mitigate interference. Numerical experiments show that the bit error rate performance of our approach is about 5 dB better than that of recent works and is closer to interference-free environment.     

硅半球深腔结构无损检测技术

半球深腔的加工质量是决定硅基MEMS半球陀螺精度的关键因素之一，及时检测半球深腔的形貌参数并将其反馈至加工过程，是确保高性能MEMS半球陀螺研制成功的重要措施。由于硅半球深腔的深度较大，台阶仪和光学显微成像系统无法对半球深腔的形貌特征进行有效测量。因此,需要将硅深腔结构剖开后采用扫描电镜(SEM)进行检测。这种检测方式时间周期长，且属于破坏性的样本检测，效率和测试精度都较低。提出以硅半球深腔为模具，利用PDMS铸模将硅半球深腔的结构尺寸和表面形貌转移到PDMS凸起的半球模型上，通过检测PDMS半球模型的尺寸结构和表面形貌，即可反推出硅半球深腔的尺寸特征。经实验验证，脱模后的PDMS模型可以准确地反映出半球深腔的尺寸信息，测量结果的不确定度小于5‰，有效解决了硅半球深腔无损检测的难题。     

某靶用型无人机发动机空中停车故障分析

针对某新型靶机出现的发动机空中停车故障,对测控站记录数据进行分析,发现了转速采集系统和ECU控制系统存在的缺陷,并提出了解决措施。     

基于CCSDS标准的航天器上行遥控链路 协议体系与可靠性技术

针对航天测控领域中上行遥控业务的协议体系选择与可靠性设计问题,在对我国现行国军标技术指标要求与现有航天测控系统天地基遥控技术特点进行归纳梳理的基础上,基于空间段信息传输无线链路特点与CCSDS(Consultative Committee for Space Data Systems,空间数据系统咨询委员会)标准规范,研究给出了适用于我国航天测控任务的空间段遥控协议体系与可靠性措施,利用梳理统计方法对上行遥控体制进行了数学建模分析,并与CCSDS给出的应用算例进行了对比分析.分析结果表明,所涉及的上行遥控体制与CCSDS标准规范的工作效能基本相当,能够满足我国航天任务上行遥控任务使用需求.     

存在外部干扰的网络控制系统故障检测和优化

针对存在时延和丢包的网络控制系统,研究了系统的故障检测滤波器设计和优化设计问题.通过泰勒展开将未知时延处理成外界干扰,采用伯努利随机过程来描述丢包现象.设计观测器产生残差信号,将故障检测问题转化为H∞滤波问题.通过Lyapunov函数方法分析了系统的稳定性.以线性矩阵不等式的形式给出了滤波器存在的充分条件和求解方法.为进一步提高系统对故障信号的灵敏度,采用了一种优化设计方法对故障检测系统进行了优化.最后通过仿真验证了算法的有效性.     

基于VALOR NPI的航空电子模块DFM分析实现

为有效解决设计缺陷造成的航空电子模块可制造性问题,结合航空电子模块产品特点和工艺技术水平,提出建设基于VALOR的航空电子模块可制造性设计自动化设计平台,实现DFM自动化分析与设计,阐述了DFM设计的必要性、技术原理和实现过程,提出了“三库”构建方法和基于物料清单的印制电路板数据分析方法,应用结果表明,提出的方法能够覆盖PCB可制造性问题,提高分析效率。     

基于离散粒子群算法的航空发动机总装工艺优化方法

为了提高航空发动机总装工作的效率与质量,加强对总装工序和总装机件的管控,量化不同总装人员工作任务量,提高发动机总装的一致性和可追溯性,提出了1种发动机总装分区优化方法。以改进的2进制粒子群算法为基础,平衡了发动机总装各区域的工作内容,最小化各总装区域的工作差异,增强了每个区域装配机件的关联度,提高了总装工作的并行度,从而提高了装配质量。以某型发动机总装区域划分工作为实例,通过上述算法进行了总装区域划分,量化了所有装配区域每一工序的具体装配内容,减小了不同装配区域间的机件差异,杜绝了缺件、剩件等装配问题。