微小故障下的深空探测航天器闭环主动故障检测

深空探测航天器是发展空间技术、扩展人类探索疆域的重要工具.航天器运行过程中一旦发生故障,极易导致探测活动失败甚至航天器损毁,而对航天器进行早期微小故障诊断,可以有效预防重大故障的发生,对于深空探测活动的顺利进行具有十分重要的意义.本文针对模型参数不确定下的深空探测航天器系统,提出一种闭环主动故障检测方法,实现对微小故障的准确检测.通过设计合适的辅助输入信号,分别注入标称和故障模型系统,使故障系统的输出与标称系统的输出无交集,以达到故障检测的目的.此外,为减小对深空探测航天器系统产生的影响,所设计的辅助信号必须尽可能小.通过对两方面需求的考虑,在传统开环研究的基础上,加入观测器建立闭环系统,在提升对微小故障的检测能力的同时减小对系统的影响.最后,利用深空探测航天器的数学仿真验证了所提闭环方法对微小故障的检测能力,并与传统开环方法进行了对比,结果表明闭环辅助信号具有更优的性能.     

基于非完备性测试的一种分布式计算机系统诊断算法的设计

利用现有的商用并行、分布式计算机系统本身所固有的冗余可以实现低成本的容错。为了提高整个分布式计算机系统的可靠性,将系统中的故障结点与正确结点隔离至关重要。文章提出了一个有效的分布式系统级故障诊断算法：在利用系统中各结点机有限的故障检测能力的基础上,将所有的故障结点从系统中隔离,并测试了该算法对系统性能的影响。     

深空探测航天器姿态控制系统故障定位

执行机构与敏感器故障检测与定位是深空探测任务卫星平台可靠运行的前提和保障.本文从数据的角度出发,结合姿控系统工作机理,提出一种基于神经网络和支持向量机结合的故障诊断方法用于检测并定位故障.故障诊断方法分为3步,首先采集姿控系统的状态信息,采用神经网络对闭环姿控系统中未知动态特性建模并进行预测;然后将姿控系统敏感器信号与神经网络预测输出比较生成残差并提取故障特征;最后采用支持向量机辨识残差特征检测故障,并结合运动学特性分析定位故障.仿真结果表明本文所提方法可以有效提取、辨识故障特征,实现执行器与敏感器的故障检测定位.     

动态系统实际故障可诊断性的量化评价研究

提出了一种新颖的动态系统实际故障可诊断性量化评价方法. 该方法无需设计任何诊断算法, 仅通过解析模型即可给出动态系统故障检测和隔离的难易程度, 从而为实现在系统设计阶段提高故障诊断能力的工程目标提供理论指导和参考依据. 首先, 通过标准化模型和等价空间变换, 将状态空间描述的随机动态系统实际故障可诊断性评价问题转化为概率统计中多元分布相似度判别的数学问题; 然后, 根据严格的数学证明, 指出距离相似度判别准则在进行可诊断性量化评价中存在的不足. 进而, 为弥补该不足, 利用故障矢量的分布概率以及不同故障矢量之间的余弦相似度, 设计基于方向相似度的可诊断性量化评价新方法; 最后, 通过数学仿真验证该方法的有效性和优越性.     

基于贝叶斯准则的航天器故障判决方法

研究航天器故障准确识别问题,由于多种因素干扰造成的航天器遥测参数观测值误差,导致故障诊断系统误判航天器状态.如何正确地从噪声信号中提取出关键的故障信号,并且根据故障发生概率及影响因素对故障进行判决,是目前航天器故障诊断领域面临的一大难题.在可获得先验的故障概率和代价系数的条件下,通过对高斯环境中故障信号误差的分析,建立了错误判决代价的贝叶斯准则判决方法,设计了高斯白噪声情况下采用贝叶斯准则进行故障判决的算法.通过某航天器实际遥测数据的实验结果表明,改进方法可显著提高系统的故障诊断准确性.     

深空探测航天器控制系统自主管理软件架构研究

深空探测航天器距离远、环境复杂,测控站遥测和遥控操作不能满足控制的实时性和安全性要求,自主管理技术是提高航天器对未知环境的应对能力、提升飞控实效性的主要手段。回顾了深空探测航天器自主管理技术发展的现状,分析了实现自主管理的关键技术,并结合深空探测工程实施和技术发展需求,提出了未来航天器自主管理系统体系结构和软件架构,并进行了仿真实验。     

含两类时滞的线性系统的故障诊断及故障可诊断性*

研究同时含有状态时滞和测量时滞的时滞系统的故障诊断方法及故障的可诊断性问题,提出一种同时对状态时滞和测量时滞进行变换的无时滞变换方法,并提出一种新的故障诊断器的构造方法,同时给出时滞系统的故障可诊断性的判据。首先通过提出一种同时对状态时滞和测量时滞进行转换的无时滞转换方法,将时滞系统转换成无时滞的系统;然后将故障诊断问题转换为状态观测问题,给出并证明了故障可诊断性的判据;最后通过构造一种不利用残差体现故障的新的故障诊断器,实现了故障的实时诊断并解决了故障诊断器的物理不可实现问题。仿真实例验证了该方法的可行     

有干扰的控制系统故障可诊断性量化评估

故障可诊断性的量化指标在控制系统设计过程中极为重要.为此,我们提出了一种适用于线性动态系统的故障可诊断性量化评估方法.考虑到过程和观测噪声等干扰因素对评估结果正确性的影响,我们采用等价空间方法获取系统输入/输出与故障之间的解析冗余关系,将故障可诊断性评估问题转化为概率统计中多元分布的差异度判别问题.引入巴氏系数(Bhattacharyya coefficient,BC)对多元分布之间的差异度进行量化,通过严格的数学证明得到可诊断性量化指标,并给出具体评估流程.以卫星姿态控制系统为仿真算例,将本文所提评估方法应用于该系统;仿真结果表明:该方法能够在不依赖于任何诊断算法的前提下,定量分析故障诊断的难易程度.     

基于自主诊断重构技术的航天器故障检测系统设计

传统航天器故障检测系统姿态定位能力较差,导致不能突破阈值,准确实现检测,且传统系统不具备重构能力;为解决上述问题,基于自主诊断重构技术,提出了一种故障检测的新方法,优化设计了航天器故障检测系统的硬件和软件部分,硬件设计采用EEC-I型检测器,为保证检测器的运行,对检测器的电压与电流范围进行了设置;设计采用MATLAB的数据采集器,选用Telnet接入端口,实现采集器的通信,确保数据的顺利采集;采用FIR滤波器,为保证信号的完整性对通带和阻带进行设置;设计采用4NIC-UPS27型号一体化不间断电源为航天器故障检测系统提供动能;软件设计基于自主诊断重构技术的航天器故障检测系统流程,运用小波网络算法对航天器的姿态角数据进行分析,预测航天器的姿态角的安全阈值,最后利用残差数据分布概率模型进行航天器故障诊断;实验结果表明,设计的基于自主诊断重构技术的航天器故障检测系统能够很好地从X、Y、Z三个轴进行检测,确定不同方位的航天器故障,在设定阈值后,提出的检测系统能够很好地分析阈值,实现残差突破,同时具备路线重构能力。     

运载火箭单惯组多表冗余的故障诊断与重构

惯性测量装置冗余是运载火箭中经常采用的一种用来提高其惯性导航系统可靠性的技术,根据冗余方式的不同,惯性测量装置冗余技术可分为系统冗余和单表冗余,分析了现有航天器惯性导航系统采用的冗余技术,从系统的重量、体积和成本方面比较,单表冗余具有明显优势;为此,从单表冗余角度介绍了一种运载火箭十表冗余的捷联惯性测量组合冗余管理方案,对冗余配置的陀螺仪和加速度计测量信息进行故障诊断,将故障定位到具体的某个陀螺仪或加速度计,在两度故障下,仍能够进行典型故障诊断,对故障仪表隔离后进行信息重构,实现一度故障及部分两度故障情况下导航信息的正常输出,增强了运载火箭惯导系统对惯组故障的容错能力。     

SpaceWire冗余网络故障检测恢复技术实现

为提高SapceWire网络可靠性,基于SpaceWire-D提出了一种应用于SpaceWire冗余网络的故障检测恢复技术。网络节点通过比较主、备份端口收到的时间码来判断链路故障状态,在确认主链路发生故障后,节点自动启用备份端口工作。通过引入时间码抖动容限参数,提高了节点对故障判断的准确性,避免了故障误判。测试结果表明,即使故障链路未与节点直接连接,节点也能够在一个时间槽长度内检测到链路故障并自动切换至备份链路。此技术保证了网络故障情况下的数据正确传输,提高了SpaceWire网络的可靠性,是一种稳定可靠的故障检测恢复技术。     

基于RBF神经网络观测器飞控系统故障诊断

为了解决非线性系统采用解析方法进行故障诊断困难的问题,利用神经网络可逼近任意连续有界非线性函数的能力,提出了一种基于RBF神经网络观测器的故障检测与诊断方法,并详细论述了该故障诊断方法的构造原理。以含有非线性项的飞行控制系统的作动器模型为例,仅作动器的输入输出可测量,通过构造RBF神经网络观测器来拟合作动器系统模型,逼近其在正常情况下的输出。最后在飞控系统的闭环控制环境下,对作动器的三种典型故障进行了计算机仿真诊断,结果表明故障诊断方法是有效的。     

基于深度迁移自编码器的变工况下滚动轴承故障诊断方法

在实际工业场景下的轴承故障诊断,存在轴承故障样本不足,训练样本与实际信号样本存在分布差异的问题;文章提出一种新的基于深度迁移自编码器的故障诊断方法FS-DTAE,应用于不同工况下的轴承故障诊断;该方法首先采用小波包变换进行信号处理与特征提取;其次,采用提出的基于朴素贝叶斯与域间差异的特征选取(FSBD)方法对统计特征进行评估,选取更有利于跨域故障诊断和迁移学习的特征;然后,利用源域特征数据训练深度自编码器,将训练得到的模型参数迁移至目标域,再利用目标域正常状态样本对深度迁移自编码器模型进行微调,微调后的模型用于目标域无标签特征数据的故障分类;最后,基于CWRU轴承故障数据开展不同工况下故障诊断实验,结果表明,所提出的FS-DTAE方法能够有效提高不同工况下的故障诊断准确率。     

Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: A deep reinforcement learning approach

Fault diagnosis methods for rotating machinery have always been a hot research topic, and artificial intelligence-based approaches have attracted increasing attention from both researchers and engineers. Among those related studies and methods, artificial neural networks, especially deep learning-based methods, are widely used to extract fault features or classify fault features obtained by other signal processing techniques. Although such methods could solve the fault diagnosis problems of rotating machinery, there are still two deficiencies. (1) Unable to establish direct linear or non-linear mapping between raw data and the corresponding fault modes, the performance of such fault diagnosis methods highly depends on the quality of the extracted features. (2) The optimization of neural network architecture and parameters, especially for deep neural networks, requires considerable manual modification and expert experience, which limits the applicability and generalization of such methods. As a remarkable breakthrough in artificial intelligence, AlphaGo, a representative achievement of deep reinforcement learning, provides inspiration and direction for the aforementioned shortcomings. Combining the advantages of deep learning and reinforcement learning, deep reinforcement learning is able to build an end-to-end fault diagnosis architecture that can directly map raw fault data to the corresponding fault modes. Thus, based on deep reinforcement learning, a novel intelligent diagnosis method is proposed that is able to overcome the shortcomings of the aforementioned diagnosis methods. Validation tests of the proposed method are carried out using datasets of two types of rotating machinery, rolling bearings and hydraulic pumps, which contain a large number of measured raw vibration signals under different health states and working conditions. The diagnosis results show that the proposed method is able to obtain intelligent fault diagnosis agents that can mine the relationships between the raw vibration signals and fault modes autonomously and effectively. Considering that the learning process of the proposed method depends only on the replayed memories of the agent and the overall rewards, which represent much weaker feedback than that obtained by the supervised learning-based method, the proposed method is promising in establishing a general fault diagnosis architecture for rotating machinery.     

A review of the diagnosability of control systems with applications to spacecraft

To achieve the safe, reliable autonomous operation of spacecraft, research on the fault diagnosis of control systems has attracted the attention of engineers and academicians throughout the aerospace field. Diagnosability can characterize the fault diagnosis capability of control systems. Connecting diagnosability analysis to the design of a spacecraft control system’s structure and diagnosis method can fundamentally improve the system’s fault diagnosis capability, improving the safety and reliability of autonomous spacecraft operation. In this paper, the diagnosability of spacecraft control systems is systematically studied from five perspectives: necessity, the current research status, the connotation, a novel index system and current development trends of diagnosability. First, the current status of spacecraft reliability analysis and reliability-based design is briefly reviewed, and existing problems are described, highlighting the advantages and importance of diagnosability research. Furthermore, the basic concepts of diagnosability are briefly introduced. By analyzing the current status of existing research on the diagnosability of both general and spacecraft control systems, the application scope of the diagnosability of spacecraft control systems is summarized. Moreover, the definition and influencing factors of the diagnosability of spacecraft control systems are presented to refine existing concepts, and a universal evaluation index system is proposed for the diagnosability of spacecraft control systems to further enhance the applicability of diagnosability evaluation and diagnosability-based design to spacecraft. Finally, the existing shortcomings and future development trends of diagnosability research for spacecraft control systems are discussed.     

Intelligent fault recognition framework by using deep reinforcement learning with one dimension convolution and improved actor-critic algorithm

The quality of fault recognition part is one of the key factors affecting the efficiency of intelligent manufacturing. Many excellent achievements in deep learning (DL) have been realized recently as methods of fault recognition. However, DL models have inherent shortcomings. In particular, the phenomenon of over-fitting or degradation suggests that such an intelligent algorithm cannot fully use its feature perception ability. Researchers have mainly adapted the network architecture for fault diagnosis, but the above limitations are not taken into account. In this study, we propose a novel deep reinforcement learning method that combines the perception of DL with the decision-making ability of reinforcement learning. This method enhances the classification accuracy of the DL module to autonomously learn much more knowledge hidden in raw data. The proposed method based on the convolutional neural network (CNN) also adopts an improved actor-critic algorithm for fault recognition. The important parts in standard actor-critic algorithm, such as environment, neural network, reward, and loss functions, have been fully considered in improved actor-critic algorithm. Additionally, to fully distinguish compound faults under heavy background noise, multi-channel signals are first stacked synchronously and then input into the model in the end-to-end training mode. The diagnostic results on the compound fault of the bearing and tool in the machine tool experimental system show that compared with other methods, the proposed network structure has more accurate results. These findings demonstrate that under the guidance of the improved actor-critic algorithm and processing method for multi-channel data, the proposed method thus has stronger exploration performance.     

A new performance measure for characterizing fault rings in interconnection networks

One of the fundamental issues in parallel computers is how to efficiently perform routing in a faulty network where each component fails with some probability. Adaptive fault-tolerant routing algorithms in such systems have been frequently suggested as a means of providing continuous operations in the presence of one or more failures by allowing the graceful system degradation. Many algorithms involve adding buffer space and complex control logic to the routing nodes. However, the addition of extra logic circuits and buffer space makes nodes more liable to failure and less reliable. Further, if the shape of fault pattern is confined, then many non-faulty nodes will be sacrificed and hence their resources are wasted. This is clearly an undesirable solution and motivates solutions that provoke efficient use of non-faulty nodes. One such approach to reducing the number of functional nodes that must be marked as faulty is based on the concept of fault rings to support more flexible routing around rectangular fault regions. Before such schemes can be successfully incorporated in networks, it is necessary to have a clear understanding of the factors that affect their performance potential. In this paper, we propose the first general solution for computing the probability of message facing the fault rings with and without overlapping in the well-known torus networks. We also conduct extensive simulation experiments using various fault patterns, the results of which are used to confirm the good accuracy of the proposed analytical models.     

基于交叉熵方法和支持向量机的模拟电路故障诊断

针对故障诊断系统中存在的大量无关或冗余的特征会严重影响故障诊断性能的缺陷,提出了基于交叉熵和支持向量机方法进行特征选择和参数优化的故障诊断方法．首先以某种概率分布产生若干随机样本,并依据交叉熵最小原理建立分布参数的更新规则进行特征搜索和ＳＶＭ 参数优化;然后利用优化后的特征向量和参数训练支持向量机获得故障诊断模型．故障诊断实验结果表明,该故障诊断方法能有效地优化故障特征和模型参数,提高故障诊断性能．