A novel approach of testability modeling and analysis for PHM systems based on failure evolution mechanism

Prognostics and health management (PHM) significantly improves system availability and reliability, and reduces the cost of system operations. Design for testability (DFT) developed concurrently with system design is an important way to improve PHM capability. Testability modeling and analysis are the foundation of DFT. This paper proposes a novel approach of testability modeling and analysis based on failure evolution mechanisms. At the component level, the fault progression-related information of each unit under test (UUT) in a system is obtained by means of failure modes, evolution mechanisms, effects and criticality analysis (FMEMECA), and then the failure-symptom dependency can be generated. At the system level, the dynamic attributes of UUTs are assigned by using the bond graph methodology, and then the symptom-test dependency can be obtained by means of the functional flow method. Based on the failure-symptom and symptom-test dependencies, testability analysis for PHM systems can be realized. A shunt motor is used to verify the application of the approach proposed in this paper. Experimental results show that this approach is able to be applied to testability modeling and analysis for PHM systems very well, and the analysis results can provide a guide for engineers to design for testability in order to improve PHM performance.     

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.     

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.     

航天器控制系统智能健康管理技术发展综述

健康管理作为智能自主控制亟待突破的关键技术之一,是提升航天器安全可靠稳定运行能力的有效手段。结合人工智能技术的发展趋势,基于前期已建立的新型航天器智能自主控制系统通用架构,详细综述航天器控制系统的智能健康管理技术现状与发展趋势。首先,根据现有航天器设计、研制和在轨的具体情况,梳理出航天器控制系统健康管理技术所面临的挑战;然后,分别从故障预警、故障诊断和寿命评估3个方面,详细阐述基于人工智能的健康管理技术研究现状及其在航天领域的应用情况;最后,提炼出航天器控制系统健康管理技术的发展方向。     

电子设备故障预测与健康管理技术发展新动态

电子设备是各类航空、航天等高新技术装备必不可少的重要组成部分。与机械类设备存在明显退化状态征兆不同,电子设备退化状态无明显的外在表现,尚无有效征兆对其状态进行刻画,对其进行故障预测与健康管理存在一定的困难。针对该问题,梳理了电子设备故障预测与健康管理技术的基本概念和内涵,介绍了电子设备故障预测与健康管理技术的国内外研究现状,分析了当前复杂电子设备故障预测与健康管理技术面临的挑战和对策。在此基础上,结合未来复杂电子设备新特点及该领域最新研究进展,从基于间歇故障特征的健康状态表征、面向故障预测与健康管理的测试性设计和多源特征融合的健康状态评估等方面,提出了电子设备故障预测与健康管理技术发展的新方向。     

基于有向图模型的民机航空电子系统故障诊断

结合民机航电系统外场可更换单元或模块（LRU/LRM）的故障模式及影响分析（FMEA）、故障树 分析和实时性要求,设计了基于时间故障传播图（TFPG）的系统故障诊断元模型和领域模型,研究了基于逻 辑和时间一致性检验的故障诊断算法,使用后向推理和可信度度量列出候选故障集。以飞行管理系统为例,构 造了其功能失效的TFPG 模型,并通过仿真验证了基于TFPG 的诊断在滤除级联效应和关联驾驶舱效应中的有 效性。     

航空发动机故障报告、分析和纠正措施系统模型的构建

针对航空发动机研制中的信息分散,不能及时纠正和管理的问题,提出构造有效的故障报告、分析和纠正措施系统平台和数据库集中管理的方法,利用该方法能够对发动机故障实施有计划、有组织、按程序地开展调查、分析和纠正的组织工作,保证故障原因分析的准确性和纠正措施的有效性,对故障实行闭环控制,彻底消除故障产生的原因,从而真正实现问题归零,为发动机研制单位成功实现高效率、高水平、高起点、归零控制等质量目标提供保障.      

无人机桨叶损伤的在线模型估计新方法

无人机在复杂高对抗环境中极易出现桨叶结构受损故障,造成无人机控制性能退化甚至失稳,给无人机带来灾难性后果。桨叶结构损伤条件下无人机动力学模型的在线估计与重建是保障无人机控制系统稳定的重要前提。由于桨叶损伤干扰隐含于无人机动力学模型的内部,可观测性较低,典型的干扰观测器难以实现对此类干扰的估计。提出了一种新型穿透型干扰观测器,通过构造穿透函数,将模型内部的干扰映射到一个新建的平行空间,实现了对桨叶损伤的估计和故障后无人机的建模,并给出了所设计穿透型干扰观测器的稳定性条件。以四旋翼无人机为应用对象,对桨叶损伤形成的干扰进行在线估计与量化,反演出桨叶随机出现的损伤,实现了桨叶损伤后无人机动力学模型的在线精细重建,解决了无人机桨叶损伤故障下力矩输入难以直接测量的问题。半物理仿真实验验证了所提方法的有效性。     

一种平纹编织复合材料的三维通用单胞模型

为了解决采用通用单胞(GMC)方法开展编织复合材料多尺度模拟的技术难题,发展了一种三维通用单胞模型,可用于二维平纹编织复合材料的多尺度模拟。概述了三维通用单胞方法的基本理论和求解流程;基于对二维平纹编织复合材料细观结构特征分析,建立了其三维通用单胞模型,并通过与传统有限元单胞模型的宏细观力学响应分析计算结果对比,验证了所建模型的正确性。采用基于通用单胞模型的有限元多尺度模拟方法,开展了二维平纹编织复合材料平板试件的模态分析。与试验结果对比表明,采用所建立的通用单胞模型预测得到的前五阶固有频率绝大部分的精度较传统有限元方法有了明显提升。该方法的优势在于可以基于通用单胞模型开展多尺度模拟,进而研究宏细观力学响应的相互关联。      

A Novel PF-LSSVR-based Framework for Failure Prognosis of Nonlinear Systems with Time-varying Parameters

Particle filtering (PF) is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters, the traditional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models. To address the problem, this paper develops a novel integrated PF-LSSVR framework based on PF and least squares support vector regression (LSSVR) for nonlinear system failure prognosis. This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances. Meantime, the propagation of prediction uncertainty is emphatically taken into account. Therefore, PF-LSSVR avoids over-dependency on system state models in prediction phase. With a two-sided failure definition, the probability distribution of system remaining useful life (RUL) is accessed and the corresponding methods of calculating performance evaluation metrics are put forward. The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.