航空发动机健康管理技术进展及趋势

综述了航空发动机健康管理技术的最新进展,并指出了其发展趋势.     

磨粒监测与分析系统在航空发动机状态监控中的应用(英文)

磨损颗粒是研究机器磨损状态时最直接、最重要的信息元 ,通过对滑油中的磨粒进行监测与分析以判断机械设备的磨损状况 ,可以监测并预防机械设备的磨损故障。本文介绍的磨粒监测与分析系统 ( DMAS)具有制谱简单、自动化程度高、能及时准确预报及诊断机器的各种磨损类故障等特点 ,可以有效地保障机器安全可靠地运行。本文详细地阐述了这套系统的基本原理、软硬件组成 ,以及在航空发动机磨损状态监测中的应用     

基于粒子滤波的结构疲劳裂纹扩展动态贝叶斯推断方法

准确预测结构的疲劳裂纹扩展过程是开展飞机单机寿命监控与剩余寿命估算的基础。提出一种基于动态贝叶斯网络的结构疲劳裂纹扩展预测方法,结合疲劳裂纹扩展的先验知识与后验知识来准确地推断裂纹长度;研究粒子滤波算法中不同粒子数对动态贝叶斯网络推断精度的影响规律;通过对单孔板结构与耳片连接结构件在随机载荷谱下进行裂纹扩展研究。结果表明：动态贝叶斯网络方法可以对复杂结构的疲劳裂纹扩展进行准确预测,预测精度相对于传统方法提高50% 以上。     

航空发动机健康管理云服务系统设计与实现

为提高航空公司的资源利用率和经营管理水平,设计了一个基于云计算的航空发动机健康管理系统。分析了航空公司对云服务系统的需求,介绍了系统的结构体系和系统开发和运行的环境。叙述了系统的功能模型和信息模型,并通过发动机健康管理一个模块——“水洗”的使用来说明系统的使用过程。     

A critique of reliability prediction techniques for avionics applications

Avionics (aeronautics and aerospace) industries must rely on components and systems of demonstrated high reliability. For this, handbook-based methods have been traditionally used to design for reliability, develop test plans, and define maintenance requirements and sustainment logistics. However, these methods have been criticized as flawed and leading to inaccurate and misleading results. In its recent report on enhancing defense system reliability, the U.S. National Academy of Sciences has recently discredited these methods, judging the Military Handbook (MIL-HDBK-217) and its progeny as invalid and inaccurate. This paper discusses the issues that arise with the use of handbook-based methods in commercial and military avionics applications. Alternative approaches to reliability design (and its demonstration) are also discussed, including similarity analysis, testing, physics-of-failure, and data analytics for prognostics and systems health management.     

签派员自我效能对工作压力源和幸福感的影响

以飞行签派员的工作角色压力源和工作幸福感为切入点，考察了飞行签派员工作压力源与工作幸福感之间的相关关系，并对飞行签派员的一般自我效能与工作压力源和工作幸福感的调节关系进行了探索。通过对309名民用航空飞行签派员进行的调查，使用相关分析与回归分析技术，发现飞行签派员工作角色压力源各维度（角色冲突、角色模糊和角色负载）与工作幸福感各维度（工作满意度、身体紧张和精神紧张）的相关系数分别在0．01水平上达到了显著，飞行签派员的工作压力源对飞行签派员的工作幸福感具有预测作用。结果显示，飞行签派员的一般自我效能感对角色冲突-工作满意度、角色冲突-精神紧张、角色负载-工作满意度、角色负载-身体紧张、角色负载-精神紧张之间的关系有显著的调节作用，而对角色模糊和工作幸福感各雏度之间的关系及对角色冲突-身体紧张之间的关系没有显著的调节作用。     

飞机健康状况量化分析方法浅析

提出与飞机健康状况相关的事件参数、风险参数的概念，具体阐述了建立量化模型的思路和方法。该方法简单可靠，具有较强的可扩展性，可为飞机的技术派遣提供决策支持。     

A data-driven health indicator extraction method for aircraft air conditioning system health monitoring

Prognostics and Health Management (PHM) has become a very important tool in modern commercial aircraft. Considering limited built-in sensing devices on the legacy aircraft model, one of the challenges for airborne system health monitoring is to find an appropriate health indicator that is highly related to the actual degradation state of the system. This paper proposed a novel health indicator extraction method based on the available sensor parameters for the health monitoring of Air Conditioning System (ACS) of a legacy commercial aircraft model. Firstly, a specific Airplane Condition Monitoring System (ACMS) report for ACS health monitoring is defined. Then a non-parametric modeling technique is adopted to calculate the health indicator based on the raw ACMS report data. The proposed method is validated on a single-aisle commercial aircraft widely used for short and medium-haul routes, using more than 6000 ACMS reports collected from a fleet of aircraft during one year. The case study result shows that the proposed health indicator can effectively characterize the degradation state of the ACS, which can provide valuable information for proactive maintenance plan in advance.     

先进战斗机强度设计技术发展与实践

新一代先进战斗机对机体平台的要求可以总结为轻重量、长寿命、多功能以及高承载。实现这个目标，除了材料与制造（新材料、新工艺、新结构/装配）的贡献，主机所强度设计/分析/验证技术也必须提升以适应先进战斗机的研制要求。本文阐述了强度设计团队围绕结构完整性要求，近年来在结构强度设计/分析/验证方面的研究成果、技术发展与设计实践，主要包括：面向新一代战斗机强度设计与验证的规范架构，基于多维包线的结构载荷筛选技术，基于统一模型的全机内力分析技术，复合材料整体化结构分析技术，高精度快速细节应力分析技术，内埋武器舱预紧舱门原理与强度设计，双曲面加筋壁板快速建模及声疲劳分析方法，结构故障预测与健康管理系统设计等。上述研究成果已成功应用于新一代战斗机机体平台研制。     

Aircraft engine fault detection based on grouped convolutional denoising autoencoders

Many existing aircraft engine fault detection methods are highly dependent on performance deviation data that are provided by the original equipment manufacturer. To improve the independent engine fault detection ability, Aircraft Communications Addressing and Reporting System (ACARS) data can be used. However, owing to the characteristics of high dimension, complex correlations between parameters, and large noise content, it is difficult for existing methods to detect faults effectively by using ACARS data. To solve this problem, a novel engine fault detection method based on original ACARS data is proposed. First, inspired by computer vision methods, all variables were divided into separated groups according to their correlations. Then, an improved convolutional denoising autoencoder was used to extract the features of each group. Finally, all of the extracted features were fused to form feature vectors. Thereby, fault samples could be identified based on these feature vectors. Experiments were conducted to validate the effectiveness and efficiency of our method and other competing methods by considering real ACARS data as the data source. The results reveal the good performance of our method with regard to comprehensive fault detection and robustness. Additionally, the computational and time costs of our method are shown to be relatively low.