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。     

高血压鉴别诊断专家系统HEDDS的不精确推理算法

本文在分析著名的医学专家系统MYCIN不精确推理算法的基础上,提出了一种易于在微型计算机上实现的简便有效的不精确推理算法,并就其一些性质进行了讨论。     

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。     

Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging

Rotating machinery is widely applied in industrial applications. Fault diagnosis of rotating machinery is vital in manufacturing system, which can prevent catastrophic failure and reduce financial losses. Recently, Deep Learning (DL)-based fault diagnosis method becomes a hot topic. Convolutional Neural Network (CNN) is an effective DL method to extract the features of raw data automatically. This paper develops a fault diagnosis method using CNN for InfRared Thermal (IRT) image. First, IRT technique is utilized to capture the IRT images of rotating machinery. Second, the CNN is applied to extract fault features from the IRT images. In the end, the obtained features are fed into the Softmax Regression (SR) classifier for fault pattern identification. The effectiveness of the proposed method is validated using two different experimental data. Results show that the proposed method has a superior performance in identification various faults on rotor and bearings comparing with other deep learning models and traditional vibration-based method.     

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.     

应用人工神经网络技术对波音747—200型飞机发动机故障诊断研究

用人工神经网络技术,对波音７４７－２００型飞机的ＪＴ９Ｄ发动机的故障诊断进行了研究,并构成了诊断装置。该研究使用北京飞机维修工程有限公司提供的发动机性能监控数据央脱机后根据性能排队情况艇经验推定法,对发动机的一些常见故障和突发性故障进行了诊断。在诊断过程中,首先搜集发动机的故障状态数据,并对这些数据进行归纳选择,制成了诊断用的教师信号“故障模型”,通过神经网络系统对教师信号的学习,在一定范围内,对     

旋转机械轴系不对中故障的建模及其理论分析

轴系不对中是旋转机械常见的故障,通过对该故障建立理论模型,并采用 Ｒａｃｃａｔｉ传递矩阵法对之进行数值计算和理论分析,重点计算了角度不对中时的振动特性。为旋转机械的故障诊断提供理论参考。