基于BP算法的液压泵在线状态监测及故障诊断

以液压泵振动信号的５个时域信息；峰值，峰峰值，均方根值，方差和波形系数作为最小诊断参数组合，用ＢＰ神经网络进行信息融合，提出一液压泵的神经网络在线状态监测及故障诊断系统。     

一种实用型报警装置的研制

本文综合地运用了５５５集成电路，ＴＷＨ９２４８，ＴＷＨ９２４９雷达式扫描探测传感集成电路的ＳＲ９Ｇ２６Ａ／Ｄ、Ｄ／Ａ转换语音存放电路等，中大规模集成电路。并利用一种简单的高频发射线路，而研制成的实用型报警装置。     

变频信号的测量方法

本文阐述了单片机测量车用电子警报器变频信号的原理和方法。     

钛合金胶接点焊接头的性能检测

研究了不同焊接电流下钛合金板胶接点焊接头的A扫描信号和C扫描图像特征,并进行了拉伸-剪切试验。结果表明:通过观察C扫描图像的特征与A扫描信号的变化,能够划分胶接点焊接头的胶层区、热影响区、熔合区、熔核区以及焊接缺陷;随着电流(7~10 k A)的逐渐增大,接头熔核直径呈递增趋势,相应的失效载荷从7 231.5增加到10 939.0 N;当电流为7 k A时,在C扫描图像上反映出飞溅缺陷,此时接头失效载荷远小于没有出现飞溅的接头,可见飞溅降低了胶接点焊接头的拉剪载荷。     

A fast pulse phase estimation method for X-ray pulsar signals based on epoch folding

X-ray pulsar-based navigation (XPNAV) is an attractive method for autonomous deep-space navigation in the future. The pulse phase estimation is a key task in XPNAV and its accuracy directly determines the navigation accuracy. State-of-the-art pulse phase estimation techniques either suffer from poor estimation accuracy, or involve the maximization of generally non-convex object function, thus resulting in a large computational cost. In this paper, a fast pulse phase estimation method based on epoch folding is presented. The statistical properties of the observed profile obtained through epoch folding are developed. Based on this, we recognize the joint prob-ability distribution of the observed profile as the likelihood function and utilize a fast Fourier transform-based procedure to estimate the pulse phase. Computational complexity of the proposed estimator is analyzed as well. Experimental results show that the proposed estimator significantly outperforms the currently used cross-correlation (CC) and nonlinear least squares (NLS) estima-tors, while significantly reduces the computational complexity compared with NLS and maximum likelihood (ML) estimators.     

Gray bootstrap method for estimating frequency-varying random vibration signals with small samples

During environment testing, the estimation of random vibration signals （RVS） is an important technique for the airborne platform safety and reliability. However, the available meth- ods including extreme value envelope method （EVEM）, statistical tolerances method （STM） and improved statistical tolerance method （ISTM） require large samples and typical probability distri- bution. Moreover, the frequency-varying characteristic of RVS is usually not taken into account. Gray bootstrap method （GBM） is proposed to solve the problem of estimating frequency-varying RVS with small samples. Firstly, the estimated indexes are obtained including the estimated inter- val, the estimated uncertainty, the estimated value, the estimated error and estimated reliability. In addition, GBM is applied to estimating the single flight testing of certain aircraft. At last, in order to evaluate the estimated performance, GBM is compared with bootstrap method （BM） and gray method （GM） in testing analysis. The result shows that GBM has superiority for estimating dynamic signals with small samples and estimated reliability is proved to be 100% at the given confidence level.     

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.     

变背景下斑点状散布目标自动识别算法

从自动目标识别原理出发,通过对变背景下斑点状散布目标特性的分析,发现了该类目标图像中背景、噪声和斑点状散布目标的固有规律。进而,从图像像素灰度这一最基本的要素着手,研究了如何在大量的序列图像中搜索目标帧位置的方法。然后,基于目标轮廓提取和串行边界分割技术,解决了变背景下斑点状散布目标自动识别的难题。实验结果证明:该算法具有高效、快速、稳定、适用等优点,并为相关领域内的应用提供了宝贵的参考价值。     

脉冲星X射线数据处理与分析

综述了脉冲星X射线数据处理的基本方法和流程,对航天器观测到的X射线数据基本格式、原始数据处理、时间分析、谱分析等内容进行了概括性描述,并以美国航空航天局(National Aeronautics and Space Administration,NASA)的X射线探测任务NuSTAR(The Nuclear Spectroscopic Telescope Array)和NICER(Neutron Star Interior Composition Explorer)为例,进行了数据处理的举例,得到了初步的处理结果,可为脉冲星导航和脉冲星天文研究提供参考。