基于虚拟样机的火炮故障仿真预测技术研究

为了实现火炮的预知维修,提高火炮执行任务的成功性,提出了基于虚拟样机的火炮故障仿真预测技术;火炮故障预测技术以当前火炮使用状态为起点,对火炮在未来任务段内可能出现的故障进行预测;建立了火炮虚拟样机,将虚拟样机作为一种定量推理方法引入到故障预测领域;提出了基于动态模糊评判的预测推理机制,基于虚拟样机实现故障过程仿真,实现了定性推理和定量推理的结合,确认实际发生的故障,找出对应的故障原因和故障部件,可以作为实现火炮预知维修的依据.     

数据驱动的故障预测方法研究

随着设备复杂性和使用环境苛刻度的不断增加,关键部件的可靠性和稳定性要求与日俱增。为实现关键部件的全面监控和故障前有效干预,故障预测技术应运而生,基于数据驱动的故障预测方法具备的适用范围广、预测精度高和建模较易等优势使其成为近年来研究的热点。论述了故障预测技术的相关理论和内涵。对当前的主流——基于数据的故障预测技术进行详细介绍,并概述了国内外的最新研究成果。最后,探讨了故障预测领域亟待解决的问题和未来的发展方向。     

基于LSSVM的电子装备故障预测研究

针对电子装备的故障信息不足,故障发生率高等特点,通过故障预测有效的监测设备故障状态以及发展趋势,实现对设备的事先维修,避免重大事故的发生,提高电子设备的安全性。对电子装备故障预测进行了分析,提出了一种基于最小二乘支持向量机（LSSVM）的故障预测方法。首先介绍了LSSVM故障预测算法的基本原理和预测流程;然后,对整个电子装备的故障预测研究可以从一个类似的模拟带通滤波器电路故障预测研究出发,将该元件容差设为不同范围来定义电路的不同故障状态,将LSSVM方法与最小二乘法、支持向量机法对电路的不同状态进行预测,可以得到不同状态的预测值,研究结果表明提出的方法能够实现模拟电路的缓变故障预测,且预测效果较好。     

基于QAR数据的民用飞机故障预测及验证

飞机飞行过程中产生成百上千种飞行参数和数量庞大的飞行数据,但目前这些数据并没有得到充分有效的利用,飞机的维修还处在以定期维修和事后维修为主的阶段。随着航空技术的不断发展,利用飞行数据进行故障预测,转变民机维修模式向视情维修发展变得越来越有必要。首先对基于QAR数据的民用飞机故障预测技术路线进行了说明。其次介绍了适用于民机QAR数据的两种故障预测方法,包括基于曲线拟合的性能预测方法和基于时间序列的趋势预测方法。再次,详细描述了基于QAR数据的故障预测系统的实现途径。通过预测关键参数变化趋势,达到提前发现故障,以制定合理的维护计划,确保飞行安全的目的。最后采用提出的方法对波音飞机的空调、滑油系统关键参数数据进行预测,预测结果验证了方法的有效性。     

基于深度学习的故障预测技术研究

针对常规故障预测方法难以分析复合故障的情况下各个故障对系统的交互作用、难以分析装备数据复杂特征、难以实时准确预测故障等现状,对现代大数据和人工智能方法应用在故障预测领域进行研究,提出基于深度学习的故障预测技术,将系统故障预测可分为动态预测和静态预测。利用深度学习算法处理装备状态监测和试验验证获得的海量故障数据,通过故障模型训练、故障特征识别、故障演化规律获取来对系统进行在线动态预测;针对软件故障突变特性,利用软件质量特征属性进行静态故障预测;同时,提出使用开源深度学习框架TensorFlow进行系统研制方法。通过基于深度学习的故障预测技术,能够提高装备故障预测能力。     

基于不确定性的故障预测方法综述

故障预测是实现视情维修策略的基础. 不确定性问题在故障预测中普遍存在, 对此, 总结了基于不确定性的故障预测方法的关键问题, 并以不确定性属性的特点将现有故障预测方法分为基于随机性、模糊性、灰性及混合不确定性等4 类. 综述了各类方法的研究现状与不足, 并展望了基于不确定性的故障预测方法的发展趋势, 探讨了基于区间不确定性的故障预测方法的可行性.

     

基于模型推理的系统级故障预测框架设计

针对装备实施故障预测过程中状态监测可达性的限制,提出系统级故障预测问题。通过参照国际标准化组织对故障预测的定义,界定系统级故障预测范畴,在此基础上应用基于模型推理的方法,构建系统级故障预测框架,实现从高层次系统监测信息中推理低层次难接近部件状态,更符合装备实际使用要求。     

基于状态维修过程中的故障预测问题分析

故障预测作为基于状态维修实现的关键,可以有效支持维修管理者的决策。参照国际标准化组织对故障预测的定义,划分故障预测过程,提出预测方法性能评估至少存在的两种方式,在分类和比较现有预测方法的基础上,明确预测方法的适用范围和限制条件,为工程预测系统的构建提供参考。     

融合案例与规则推理的故障预测专家系统

根据故障预测的特点和要求，将案例推理引入到故障预测专家系统的设计中，阐述了案例的描述和检索方法。并以全新的角度提出了基于案例与规则的故障预测推理机制，对二者的结合方式作了阐述，给出了专家系统的整体实现结构。     

基于PCA故障重构的烟气轮机故障预测

主元分析作为一种统计分析方法,被广泛的用于过程监控中。文中假设故障可完全重构,利用基于平方预测误差的故障重构方法对故障幅值进行估计,并采用多层递阶方法对估计出的故障幅值进行预测。以北京燕山石化公司炼油厂的烟气轮机组作为研究对象,验证了该方法的有效性。     

新一代航空电子综合化及预测与健康管理技术

在介绍航空电子系统发展演变历史的基础上,分析了新一代综合化航电系统的主要特征,深入探讨了航空电子预测与健康管理(PHM)的关键技术,包括失效机理分析、应力损伤评估、故障先兆以及预警电路(canary)4个类别,详细分析了PHM相关的信息融合、人工智能、数据挖掘与预测等技术的实现方法与应用,研究并给出了新一代综合模块化航...     

基于协议无感知转发的服务功能链

IETF提出的服务功能链（Service Function Chain,SFC）解决了服务功能在部署过程中网络拓扑与硬件设备紧密耦合、灵活性差等问题,其中,NSH协议用于支持服务功能链的实现.然而,标准的OpenFlow协议对NSH协议支持不足、实现过程复杂且实现后造成兼容性等问题.本文基于软件定义网络（Software Defined Network,SDN）和网络功能虚拟化（Network Function Virtualization,NFV）技术,根据IETF规定的相关标准,提出一种基于协议无感知转发（Protocol Oblivious Forwarding,POF）的服务功能链,利用POF在数据平面深度可编程的能力实现NSH协议.文中基于FloodLight控制器和POF交换机实现了该服务功能链,实验结果表明,基于协议无感知转发的服务功能链可以高效地实现服务功能的部署.     

一种电子装备故障预测参数选取方法

针对电子装备故障预测中的预测参数选取问题,建立电子装备故障预测信息系统,设置故障预测参数选取原则,基于多信号流图模型提取反映电子装备故障状态的预测参数集,利用决策辨识函数对该参数集进行约简,并采用基于相关危险度的统计模型选取出最优故障预测参数集。实例证明该方法可以实现预测参数选取的自动化。     

A novel multi-objective optimization algorithm based on Lightning Attachment Procedure Optimization algorithm

In this paper, a novel multi-objective optimization method based on a recently introduced algorithm known as Lightning Attachment Procedure Optimization (LAPO) is presented. The proposed algorithm is based on non-dominated sorting approach where the best solutions chosen from the Pareto Optimal Front (POF), based on crowding distance, are stored in a repository matrix called an Archive matrix. The procedure is performed such that the final best solutions are distributed evenly along the optimal PF. Then, the proposed algorithm is tested by some multi-objective optimization functions and some classical engineering problems also. The results are compared to those of four well-known methods and then discussed. The results are compared using 4 criteria which show how to select a POF close to the true POF, how the results are distributed, and how close the final results approximate all the possible outcomes of true POF. It is shown that the proposed method outperforms the other methods with regards to 3 criteria and yields comparable results regarding the last criteria. Superiority of the proposed method in finding the true POF while covering a wide range of possible optimal results is discussed in the results section. Therefore, it is concluded that the proposed method does an excellent job at solving a wide range of multi-objective optimization problems.     

A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction

Remaining useful life prediction is one of the key requirements in prognostics and health management. While a system or component exhibits degradation during its life cycle, there are various methods to predict its future performance and assess the time frame until it does no longer perform its desired functionality. The proposed data-driven and model-based hybrid/fusion prognostics framework interfaces a classical Bayesian model-based prognostics approach, namely particle filter, with two data-driven methods in purpose of improving the prediction accuracy. The first data-driven method establishes the measurement model (inferring the measurements from the internal system state) to account for situations where the internal system state is not accessible through direct measurements. The second data-driven method extrapolates the measurements beyond the range of actually available measurements to feed them back to the model-based method which further updates the particles and their weights during the long-term prediction phase. By leveraging the strengths of the data-driven and model-based methods, the proposed fusion prognostics framework can bridge the gap between data-driven prognostics and model-based prognostics when both abundant historical data and knowledge of the physical degradation process are available. The proposed framework was successfully applied on lithium-ion battery remaining useful life prediction and achieved a significantly better accuracy compared to the classical particle filter approach.     

Source Routing Over Protocol-Oblivious Forwarding for Named Data Networking

Named data networking (NDN) has promising advantages, however, it is hard to deploy because of the lack of special hardware to support it. Besides, the lack of specific routing schema also restrains the advantages of NDN. To solve the problems of deployment and routing, we come up with the source routing schema over protocol-oblivious forwarding (POF) for NDN. POF is able to directly carry NDN packets without over IP because it provides an adaptable and programmable forwarding plane by defining a generic flow instruction set. Source routing is a flexible and scalable routing schema and provides the flexible and efficient implementation for the future network. The centralized management of the source routing schema over POF can provide the following properties: (i) quickly responding to the change of network, and (ii) forwarding the Interest packet to the best cache node. Experiments show that the source routing schema over POF for NDN can reduce more than 50% of the networking traffic flow and increase the cache hit rate by up to 25% more than the baseline schemas. Thus, the source routing schema over POF could be an implementation plan for NDN.     

基于测试性的电子系统综合诊断与故障预测方法综述

故障诊断与预测技术是故障预测与健康管理(PHM)中的两大关键技术.依据电子系统的故障模式与机理,结合测试性设计分析理论,提出了一种基于测试性的电子系统综合诊断与故障预测方法框架.对国内外综合诊断与故障预测方法进行了分类与总结,从基于测试性的嵌入式诊断、基于信号处理的智能故障诊断、基于测试性的故障预测3个方面论述了电子系统综合诊断与故障预测方法.最后分析了制约电子系统综合诊断与故障预测的因素,并探讨了未来的发展趋势.     

A multi-branch deep neural network model for failure prognostics based on multimodal data

Non-numerical data, such as images and inspection records, contain information about industrial system degradation, but they are rarely used for failure prognostic tasks given the difficulty of automatic analysis. In this work, we present a novel method for prognostics using multimodal data, i.e. both numerical and non-numerical data. The proposed method is based on the development of a multi-branch Deep Neural Network (DNN), each branch of which is a neural network designed for processing a certain type of data. The method is applied to a case study properly designed to reproduce the problem of prognostics using multimodal data by referring to the operation of steam generators. The results show that it is able to accurately predict future degradation level using multimodal data, outperforming other methods using fewer sources of information.     

故障预测与健康管理系统建模技术研究

简要介绍了PHM系统建模的主要方法,并依据飞机特征,给出飞机PHM系统分层体系模型,提出了基于数据的飞机典型部件液压泵PHM系统的建模方法,通过试验对该方法进行了验证.     

Fault features extraction for bearing prognostics

Over the past years, investigation on condition-based maintenance (CBM) technique on bearing has been conducted. Bearing diagnostics and prognostics are the important aspects in CBM. A key to the success of using vibration data for bearing fault diagnostics and bearing lifecycle prognostics is a quantified relationship between bearing damage and bearing fault features. To establish such a quantitative relationship, effective signal processing techniques to extract bearing fault features from vibration signals are needed. This paper describes a newly developed fault feature extraction method for bearing prognostics. The effectiveness of the method is demonstrated with two real bearing run-to-failure test datasets: one collected under normal operating conditions and another one under abnormal operating conditions. Experimental results show that the bearing fault features extracted using both traditional vibration analysis methods and the proposed method give clear bearing heath degradation trend for the dataset collected under normal operating conditions. However, for the data collected under abnormal operating conditions, bearing fault features obtained using traditional vibration analysis methods fail to show the bearing health degradation trend while the fault features extracted using the proposed method give consistent bearing degradation trends.