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.     

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。     

飞机结构健康监测与管理技术研究进展和展望

飞机结构是飞机平台的基础,是确保飞机安全、长寿命使用的最重要的承力架构.随着航空技术的不断发展,飞机设计思想不断演变发展,对飞机机体结构性能也不断提出更高的要求.严酷的使用环境和严格的功能/性能综合要求,使得结构完整性面临重大挑战.飞机结构健康监测与管理技术具有实时性、在线性等优势,是保证结构安全性、降低维护费用的重要...     

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.     

航空发动机健康管理系统的快速原型设计

针对航空发动机健康管理系统传统设计方法周期长、成本高等问题,提出了面向健康管理系统的快速原型设计方法,构建了基于虚拟仪器语言和快速原型技术的航空发动机健康管理系统快速原型仿真平台。以CompactRIO平台为核心,在实时响应最高的现场可编程门阵列(FPGA)环境下设计了信号接口单元,模拟发动机真实传感器值,并提供了基于Windows平台的健康管理软件实时开发环境,可以将健康管理算法快速部署下载至硬件平台。结果表明:此健康管理系统的快速原型设计方法是切实有效的,为扩展成为完全的硬件在回路(HIL)仿真的平台奠定了基础,有较好的工程实用价值。