液体火箭发动机泄漏故障诊断的信息融合技术

从故障诊断角度出发,分析了故障诊断和信息融合技术关系。综合现在进行液体火箭发动机检漏的相关技术,为了弥补泄漏故障诊断信息不足等问题,提出了采用点式和红外传感器相结合的方法来探测泄漏故障引起的各种现象,概述了该领域的相关技术和实现方法,建立了基于信息融合的泄漏故障检测与诊断系统,有利于提高泄漏故障的有效性和可信度。      

Optimal Data Fusion in Multiple Sensor Detection Systems

There is an increasing interest in employing multiple sensors for surveillance and communications. Some of the motivating factors are reliability, survivability, increase in the number of targets under consideration, and increase in required coverage. Tenney and Sandell have recently treated the Bayesian detection problem with distributed sensors. They did not consider the design of data fusion algorithms. We present an optimum data fusion structure given the detectors. Individual decisions are weighted according to the reliability of the detector and then a threshold comparison is performed to obtain the global decision.     

Optimal Decision Fusion in Multiple Sensor Systems

The problem of optimal data fusion in the sense of the Neyman-pearson (N-P) test in a centralized fusion center is considered. The fusion center receives data from various distributed sensors. Each sensor implements a N-P test individually and independently of the other sensors. Due to limitations in channel capacity, the sensors transmit their decision instead of raw data. In addition to their decisions, the sensors may transmit one or more bits of quality information. The optimal, in the N-P sense, decision scheme at the fusion center is derived and it is seen that an improvement in the performance of the system beyond that of the most reliable sensor is feasible, even without quality information, for a system of three or more sensors. If quality information bits are also available at the fusion center, the performance of the distributed decision scheme is comparable to that of the centralized N-P test. Several examples are provided and an algorithm for adjusting the threshold level at the fusion center is provided.     

Data fusion of multiradar system by using genetic algorithm

Detection system with distributed sensors and data fusion. are increasingly being used by surveillance systems. There has been a great deal of theoretical study on decentralized detection networks composed of identical or non-identical sensors. To solve the resulting nonlinear system, exhaustive search and some crude approximations are adopted. However, those methods often cause either the system to be insensitive to some parameters or the suboptimal results. In this paper, a novel flexible genetic algorithm is investigated to obtain the optimal results on constant false alarm rate data fusion. Using this approach, all system parameters are directly coded in decimal chromosomes and they can be optimized simultaneously. The simulation results show that adopting the proposed approach, one can achieve better performances than the reported methods and results     

Optimal distributed decision fusion

The problem of decision fusion in distributed sensor systems is considered. Distributed sensors pass their decisions about the same hypothesis to a fusion center that combines them into a final decision. Assuming that the sensor decisions are independent of each other for each hypothesis, the authors provide a general proof that the optimal decision scheme that maximizes the probability of detection at the fusion for fixed false alarm probability consists of a Neyman-Pearson test (or a randomized N-P test) at the fusion and likelihood-ratio tests at the sensors     

Nonstochastic adaptive decision fusion in distributed-detection systems

Implementing the optimal Neyman-Pearson (NP) fusion rule in distributed detection systems requires the sensor error probabilities to be a priori known and constant during the system operation. Such a requirement is practically impossible to fulfil for every resolution cell in a multiflying target multisensor environment. The true performance of the fusion center is often worse than expected due to fluctuations of the observed environment and instabilities of sensor thresholds. This work considers a nonparametric data fusion situation in which the fusion center knows only the number of the sensors, but ignores their error probabilities and cannot control their thresholds. A data adaptive approach to the problem is adopted, and combining P reports from Q independent distributed sensors through a least squares (LS) formulation to make a global decision is investigated. Such a fusion scheme does not entail strict stationarity of the noise environment nor strict invariance of the sensor error probabilities as is required in the NP formulation. The LS fusion scheme is analyzed in detail to simplify its form and determine its asymptotic behavior. Conditions of performance improvement as P increases and of quickness of such improvement are found. These conditions are usually valid in netted radar surveillance systems.     

CFAR data fusion center with inhomogeneous receivers

Detection systems with distributed sensors and data fusion are increasingly used by surveillance systems. A system formed by N inhomogeneous constant false alarm rate (CFAR) detectors (cell-averaging (CA) and ordered statistic (OS) CFAR detectors) is studied. A recursive formulation of an algorithm that permits a fixed level of false alarms in the data fusion center is presented, to set the optimum individual threshold levels in the CFAR receivers and the optimum `K out of N' decision rule in order to maximize the total probability of detection. The algorithm also considers receivers of different quality or with different communication channel qualities connecting them with the fusion center. This procedure has been applied to several hypothetical networks with distributed CA-CFAR and OS-CFAR receivers and for Rayleigh targets and interference, and it was seen that in general the fusion decision OR rule is not always the best     

Detection and diagnosis of sensor and actuator failures using IMMestimator

An approach to detection and diagnosis of multiple failures in a dynamic system is proposed. It is based on the interacting multiple-model (IMM) estimation algorithm, which is one of the most cost-effective adaptive estimation techniques for systems involving structural as well as parametric changes. The proposed approach provides an integrated framework for fault detection, diagnosis, and state estimation. It is able to detect and isolate multiple faults substantially more quickly and more reliably than many existing approaches. Its superiority is illustrated in two aircraft examples for single and double faults of both sensors and actuators, in the forms of “total”, “partial”, and simultaneous failures. Both deterministic and random fault scenarios are designed and used for testing and comparing the performance fairly. Some new performance indices are presented. The robustness of the proposed approach to the design of model transition probabilities, fault modeling errors, and the uncertainties of noise statistics are also evaluated     

Fusion of distributed extended forgetting factor RLS state estimators

For single-target multisensor systems, two fusion methods are presented for distributed recursive state estimation of dynamic systems without knowledge of noise covariances. The estimator at every local sensor embeds the dynamics and the forgetting factor into the recursive least squares (RLS) method to remedy the lack of knowledge of noise statistics, developed before as the extended forgetting factor recursive least squares (EFRLS) estimator. It is proved that the two fusion methods are equivalent to the centralized EFRLS that uses all measurements from local sensors directly and their good performance is shown by simulation examples.     

Ultra-reliable real-time control systems-future trends

Today's aircraft use ultra-reliable real-time controls for demanding functions such as Fly-By-Wire (FBW) flight control. Future aircraft, spacecraft and other vehicles will require greater use of these types of controls for functions that currently are allowed to fail, fail to degraded operation, or require human intervention in response to failure. Fully automated and autonomous functions will require ultra-reliable control. But ultra-reliable systems are very expensive to design and require large amounts of on-board equipment. This paper will discuss how the use of low-cost sensors with digital outputs, digitally commanded fault-tolerant actuation devices and interconnecting networks of low-cost data buses offer the promise of more affordable ultra-reliable systems. Specific technologies and concepts to be discussed include low-cost automotive and industrial data buses, “smart” actuation devices with integral fault masking capabilities, management of redundant sensors, and the fault detection and diagnosis of the data network. The advantages of integrating the control and distribution of electrical power with the control system will be illustrated. The design, installation, and upgrade flexibility benefits provided by an all-digital and shared network approach will be presented. The economic benefits of systems that can operate following failure and without immediate repair will be reviewed. The inherent ability of these redundant systems to provide effective built-in test and self-diagnostics capabilities will be described. The challenges associated with developing ultra-reliable software for these systems and the difficulties associated with exhaustive verification testing will be presented as will additional development hurdles that must be overcome     

基于SPSO-SVR的融合航空发动机传感器故障诊断

针对航空发动机常见的传感器故障问题, 提出了一种利用改进的粒子群算法训练支持向量回归机, 并利用融合机制将其应用于传感器故障诊断.论述了用一簇支持向量回归机(SVR)预测器对传感器进行实时检测, 通过逻辑判断机制隔离故障传感器, 并且依据剩余的无故障传感器信息实现信号重构.以某型航空发动机传感器在其整个工作范围内受到的冲击、偏置和漂移故障为例, 验证了基于自协调粒子群优化支持向量回归机(SPSO-SVR)算法的融合诊断机制对传感器单一故障和多重故障具有较高的精度和计算效率.      

一种基于相邻模块化加权D-S的融合诊断方法

常规D-S (Dempster-Shafter)决策融合方法由于其自身理论不足,不能很好直接处理决策结果偏差大、冲突大的传感器融合问题,因而对于信息高冲突情况下的转子微弱故障融合诊断存在着失效问题。针对该类问题与不足,借鉴复杂网络的舆论传播、社会学习理论及多智能体一致性决策的相关概念与思路,从避免决策结果冲突大的传感器直接进行融合的角度进行改进,提出相邻模块化加权D-S融合方法。该方法首先根据初步结果进行相邻节点与模块划分,只有决策距离在相邻界限值范围内的相邻模块节点才能进行决策融合;对于同一模块内相邻节点,根据各节点决策权重及初步决策结果采用加权D-S融合方法进行决策融合;针对融合结果再进行相邻节点模块划分与融合,依此步骤进行循环划分与融合,直到所有模块与节点均不相邻;最后采用专家权威决策方法确定权重和最大的模块融合结果作为最终的传感器网络一致性决策结果。通过多传感器网络的转子故障模拟实验对所提方法进行验证,应用结果表明:所提方法可以较好解决少数传感器诊断正确、而多数诊断错误的信息高冲突条件下的局部微弱故障融合诊断问题。     

基于UIO的航空发动机控制系统传感器故障诊断

研究了航空发动机控制系统传感器鲁棒故障检测与隔离问题,提出了一种克服不同干扰对控制系统诊断性能影响的方法.应用未知输入观测器(unknown input observer,简称UIO)理论来解耦航空发动机动态系统干扰,并针对控制系统传感器设计一族UIO,提取出一系列的传感器残差特征数据,通过分析残差队列的幅值特性,实现航空发动机控制系统传感器故障诊断.在高斯白噪声、模型工作点变化和非高斯噪声三类干扰下的数字仿真结果表明,不管何种干扰,UIO诊断方法均能检测和隔离出传感器故障,在诊断鲁棒性方面,要优于Kalman滤波器诊断算法.      

Detection and range/Doppler estimation for colocated sensors

The problem of multisensor detection and high resolution signal state estimation using joint maximum a posteriori detection and high order nonlinear filtering techniques is addressed. The model-based fusion approach offers the potential for increased target resolution in range/Doppler/azimuth space. The approach employs joint detection/estimation filters (JDEF) for target detection and localization. The JDEF approach segments the aggregate nonlinear model over the entire target resolution space into a number of localized nonlinear models by partitioning the resolution space into a number of resolution subcells. This partitioning leads to extremely accurate state estimation. The proposed JDEF approach has a built-in capability for automatic data alignment from multiple sensors, and can be used for centralized, decentralized, and distributed data fusion.     

基于奇偶方程的FADS传感器故障检测方法

重复使用运载器（RLV）的嵌入式大气数据传感系统（FADS）中传感器的高可靠性是RLV飞行控制系统高可靠性的保障。结合FADS采用多个测压点冗余配置的特点,利用各传感器测量值之间存在的解析冗余关系,设计奇偶方程,实现对各个测压点故障传感器的有效检测。     

一种分布式异步航迹融合算法

在多被动传感器目标跟踪中,融合中心处理的信息一般是同步的,然而实际情况并非如此。另外,一些被动传感器只能得到目标的方位信息,无法单独形成有效航迹,这就需要将各传感器数据同步到相同时刻,然后应用同步融合算法。针对被动传感器探测系统,采用传感器到传感器融合和系统到传感器融合的分布式融合结构,并对各局部传感器引入全局反馈,对相关信息采用协方差交叉算法进行处理,完成被动传感器异步数据的融合,仿真结果表明,该算法具有较好的融合效果。     

多传感器融合状态估计方法综述

随着传感器网络技术的发展，多传感器融合状态估计凭借其鲁棒性、灵活性、可扩展性以及便于故障检测等优点，长期受到国内外学者的广泛关注，并取得了大量研究成果。数据融合的方法为融合状态估计奠定了理论基础，也是早期研究的主要方向，从20世纪70年代到20世纪末，相继发展出了集中式和分散式滤波架构及相应算法。无线通信技术的成熟以及一致性算法的出现使得分布式状态估计的研究进入了快车道，自2005年以来，大量基于一致性的分布式滤波算法被提出，其中不乏实用的经典方法和优秀的开创性方法。旨在梳理多传感器融合状态估计的发展，探究从数据融合到分布式滤波的内在联系，并对一些经典方法进行了总结。     

基于数据分发服务动态发现的通信监控设计

数据分发服务（DDS）在复杂航电系统中的应用越来越广泛，其以数据为中心的发布/订阅机制，解决了复杂系统异构、分布式、动态扩展、互操作和可移植的问题，同时也带来了系统描述和通信监控的困难。基于DDS实体动态发现的原理，提出了一种能够动态构建应用主题类型化接口，实现对网络中DDS实体间通信进行监控的通用设计方法。该方法能够满足基于DDS网络的通信监控需求，提高复杂系统快速集成和联调排故的效率。     

非线性系统中多传感器目标跟踪融合算法研究

 研究了在非线性系统中 ,基于转换坐标卡尔曼滤波器的多传感器目标跟踪融合算法。通过分析得出 :在非线性系统的多传感器目标跟踪中 ,基于转换坐标卡尔曼滤波器 ( CMKF)的分布融合估计基本可以重构中心融合估计。仿真实验也证明了此结论。由此可见分布的 CMKFA是非线性系统中较优的分布融合算法     

Detection with Distributed Sensors

The extension of classical detection theory to the case of distributed sensors is discussed, based on the theory of statistical hypothesis testing. The development is based on the formulation of a decentralized or team hypothesis testing problem. Theoretical results concerning the form of the optimal decision rule, examples, application to data fusion, and open problems are presented.