小波技术在组合导航系统故障诊断中的应用

针对组合导航系统中存在的故障类型和特点，提出一种新的基于信号处理的故障检测方法。该方法利用小波分析技术对含有故障信息的系统信号进行分析处理，并将此方法应用于SINS／GPS组合导航系统。仿真结果分析表明，小波分析技术能很好地及时检测出组合导航系统的硬故障，且更加简便灵活，从而可以将小波分析技术应用于组合导航系统的故障检测。     

A discrete event systems approach to discriminating intermittent from permanent faults

Almost all work on model-based diagnosis （MBD） potentially presumes faults are per- sistent and does not take intermittent faults （IFs） into account. Therefore, it is common for diag- nosis systems to misjudge IFs as permanent faults （PFs）, which are the major cause of the problems of false alarms, cannot duplication and no fault found in aircraft avionics. To address this problem, a new fault model which includes PFs and IFs is presented based on discrete event systems （DESs）. Thereafter, an approach is given to discriminate between PFs and IFs by diagnosing the current fault. In this paper, the regulations of （PFs and IFs） fault evolution through fault and reset events along the traces of system are studied, and then label propagation function is modified to account for PFs and the dynamic behavior of IFs and diagnosability of PFs and IFs are defined. Finally, illustrative examples are presented to demonstrate the proposed approach, and the analysis results show the fault types can be discriminated within bounded delay if the system is diagnosable.     

三角带失效概率模型

通过失效物理分析，考虑了挠性传动件-三角带的受力和变形．剖析了其失效形式．根据其特殊的失效物理背景推导了数学模型，提出宜采用最弱环模型，大样本试验证实了上述分析。     

探析新《道路交通安全法》中法律归责原则及完善问题

2004年5月1日开始实施的《中华人民共和国道路交通安全法》针对机动车与机动车之间、机动车与非机动车驾驶人或行人之间的交通事故，分别确立了过错责任和无过错责任两种不同的归责原则。本文拟运用侵权行为法归责原则理论，通过国内外有关法律之比较分析及我国新旧交通安全法规对道路交通事故责任承担问题展开讨论，以求抛砖引玉。     

大力神—4改进型固体助推器爆炸原因分析及启示

介绍了大力神-4改进型固体助推器的研制情况、结构特点及主要性能,阐述了地面试验失败的原因及改进措施,并联系卡斯托Ⅱ研制中出现的类似失败情况,就发动机内流场气流的相互作用及药柱变形对发动机工作的影响进行了扼要分析,并提出了值得重视的几点启示。     

火箭发动机基于神经网络非线性辨识的故障检测

应用神经网络方法，提出了一种液体火箭发动机故障实时检测算法。神经网络采用非线性辨识技术贴近发动机的工作过程，并输出包合发动机故障信息的辨识误差信号。若辨识误差变大超过一定阈值，检测逻辑就预报发动机故障。在发动机启动阶段离线训练神经网络，在发动机稳态过程可以采用离线或在线学习算法。实验研究表明神经网络可以成功地应用于大型泵压式液体火箭发动机的故障检测。     

A new bearing fault diagnosis method based on modified convolutional neural networks

Fault diagnosis is vital in manufacturing system. However, the first step of the traditional fault diagnosis method is to process the signal, extract the features and then put the features into a selected classifier for classification. The process of feature extraction depends on the experimenters’ experience, and the classification rate of the shallow diagnostic model does not achieve satisfactory results. In view of these problems, this paper proposes a method of converting raw signals into two-dimensional images. This method can extract the features of the converted two-dimensional images and eliminate the impact of expert’s experience on the feature extraction process. And it follows by proposing an intelligent diagnosis algorithm based on Convolution Neural Network (CNN), which can automatically accomplish the process of the feature extraction and fault diagnosis. The effect of this method is verified by bearing data. The influence of different sample sizes and different load conditions on the diagnostic capability of this method is analyzed. The results show that the proposed method is effective and can meet the timeliness requirements of fault diagnosis.     

Neural network-based fault diagnosis for spacecraft with single-gimbal control moment gyros

This paper proposes a neural network-based fault diagnosis scheme to address the problem of fault isolation and estimation for the Single-Gimbal Control Moment Gyroscopes(SGCMGs) of spacecraft in a periodic orbit. To this end, a disturbance observer based on neural network is developed for active anti-disturbance, so as to improve the accuracy of fault diagnosis.The periodic disturbance on orbit can be decoupled with fault by resorting to the fitting and memory ability of neural network. Subsequ...     

SAR performance-based fault diagnosis for electro-hydraulic control system: A novel FDI framework for closed-loop system

Model-based fault diagnosis serves as an efficient and powerful technique in addressing fault detection and isolation (FDI) issues for control systems. However, the standard methods and their modifications still encounter some difficulties in algorithm design and application for complex higher-order systems. To avoid these difficulties, a novel fault diagnosis framework based on multiple performance indicators of closed-loop control system is proposed. Under this framework, a so-called performance residual vector is constructed to measure the differences between the real system and the nominal model in terms of system stability, accuracy, and rapidity (SAR) respectively. The criteria for quantification, normalization of the SAR residuals and the explicit mappings between the thresholds and the required performance are given. FDI can be easily achieved simultaneously by monitoring the normalized residual vector length and direction in the SAR performance residual space. A case study on electro-hydraulic servo control system of turbofan engine is adopted to demonstrate the effectiveness of the proposed method.     

基于故障树和神经网络模型的航天器智能诊断研究

提出基于故障树和神经网络模型的诊断方法,提出面向故障树的基于框架和广义规则的知识表示方法及相应的确定性和可能性推理策略,对于可能性推理的结果,通过基于神经网络模型的学习诊断来进一步确定其状态。在Ｗｉｎｄｏｗｓ环境下,用Ｂｏｒｌａｎｄ Ｃ＋＋实现了一个原型系统。通过对“实践４号”卫星能源系统故障模拟实验台的诊断验证了系统的有效性。