基于组态王6.5的离心泵微机在线监控系统

离心输油泵是石油化工产品输送的主要设备,微机在线监控系统主要用于炼油厂、乙烯厂、油库、输油管道等油气输送领域离心输油泵运行状态的监测、控制以及检修的预报等。本设计采用操作指导控制系统以及工厂的分布式层次化结构思想,以离心输油泵输入输出轴的振动参数为主要测试参数,并辅以离心输油泵油温、轴瓦温度监测,同时采用ADAM4000智能模块和RS—485网络,通过监控控制仪转换成RS—232信号给计算机,实现所需参数的显示与控制和计算机对离心输油泵的快速启停控制。系统软件基于Windows XP平台,运用北京亚控组态王6.5版开发而成,具有实用、实时、可靠与友好等特点。     

Structure damage diagnosis using neural network and feature fusion

A structure damage diagnosis method combining the wavelet packet decomposition, multi-sensor feature fusion theory and neural network pattern classification was presented. Firstly, vibration signals gathered from sensors were decomposed using orthogonal wavelet. Secondly, the relative energy of decomposed frequency band was calculated. Thirdly, the input feature vectors of neural network classifier were built by fusing wavelet packet relative energy distribution of these sensors. Finally, with the trained classifier, damage diagnosis and assessment was realized. The result indicates that, a much more precise and reliable diagnosis information is obtained and the diagnosis accuracy is improved as well.     

Automatic detection of breast cancers in mammograms using structured support vector machines

Breast cancer is one of the most common cancers diagnosed in women. Large margin classifiers like the support vector machine (SVM) have been reported effective in computer-assisted diagnosis systems for breast cancers. However, since the separating hyperplane determination exclusively relies on support vectors, the SVM is essentially a local classifier and its performance can be further improved. In this work, we introduce a structured SVM model to determine if each mammographic region is normal or cancerous by considering the cluster structures in the training set. The optimization problem in this new model can be solved efficiently by being formulated as one second order cone programming problem. Experimental evaluation is performed on the Digital Database for Screening Mammography (DDSM) dataset. Various types of features, including curvilinear features, texture features, Gabor features, and multi-resolution features, are extracted from the sample images. We then select the salient features using the recursive feature elimination algorithm. The structured SVM achieves better detection performance compared with a well-tested SVM classifier in terms of the area under the ROC curve.     

Visual Prolog在专家系统故障诊断中的应用

针对故障诊断的特点,利用Visual Prolog语言在知识表示及逻辑推理过程中的优势,以流程工业中易发生事故的催化裂化装置为例设计并实现了基于规则的故障诊断专家系统。用事实和规则这2种构成Visual Prolog语言的短语进行知识库和推理机的编写。通过在催化裂化生产装置的实际应用表明,该诊断系统效果良好,能够对出现的多数异常工况进行有效的推理及分析,及时查找出故障原因,并提供相应的故障处理措施。     

基于信息融合技术的瓦斯传感器故障诊断研究

文章提出了将基于RBF网络的信息融合技术应用于瓦斯传感器故障诊断的思想。该思想的核心是通过对影响测点瓦斯浓度的各种相关信息融合,利用高精度RBF网络逼近器的输出与瓦斯传感器实际的输出之差与设定的阈值比较,实现瓦斯传感器故障的监测诊断。试验表明该技术能对瓦斯传感器进行有效的状态监测和故障诊断。     

A mass spectrometric strategy for profiling glycoproteinoses, Pompe disease, and sialic acid storage diseases

Glycoproteinoses, Pompe disease, and sialic acid storage diseases are characterized by a massive accumulation of unprocessed oligosaccharides and/or glycoconjugates in urine. The identification of these glycocompounds is essential for a proper diagnosis. In this study, we investigated the potential of MALDI‐TOF‐MS to identify glycocompounds present in urine from patients with different inborn errors of glycan metabolism. Urinary glycocompounds were permethylated, and analyzed using GC‐MS and MALDI‐TOF‐MS. In order to confirm tentative assignments, a second aliquot of urine was purified on a C18 Sep‐Pak cartridge and glycocompounds were desalted on a column of nonporous graphitized carbon. The glycocompounds were then sequentially on‐plate digested using an array of exoglycosidases. A range of disease‐specific oligosaccharides as well as glycopeptides was identified for all oligosacchariduria models. In addition, free sialic acid accumulated in urine from a patient suffering from French‐type sialuria, has been detected by a GC‐MS approach, which could be applied to other sialic acid storage diseases. This procedure is simple, and can be performed in few simple steps in less than 24 h. This current method can be applied for newborn screening for other inherited metabolic diseases as well as for assessing treatments in clinical trials.     

State Observation and Diagnosis of Discrete-Event Systems Described by Stochastic Automata

The problems ofstate observation and diagnosis are solved for discrete–eventsystems, which are described by stochastic automata. As manysystems are not observable in the sense that it is possible toreconstruct the state unambiguously, the observation problemis set up as the problem of determining the smallest possibleset of states that are compatible with the measured input andoutput sequences. The diagnostic problem is shown to be, in principle,an observation problem. Conditions for the observability anddiagnosability of stochastic automata are presented. The resultsare illustrated by examples.     

知识获取技术及其在网络智能故障诊断中的应用

基于遗传算法提出了一种新型的机器学习方法,它利用遗传算法的全局寻优手段,为解决知识获取这一瓶颈问题另辟捷径。论述了该方法的原理及其实现过程,并且给出了网络故障诊断应用实例,证明了它的有效性。     

Model-based cognitive diagnosis

This paper considers the problem of cognitive diagnosis as an instance of general diagnosis, as studied in artificial intelligence. Cognitive diagnosis is the process of inferring a cognitive state from observations of performance. It is thus a key component of any system which attempts to build a dynamic model of the user of that system. Many issues in cognitive diagnosis, previously discussed informally, are mapped onto formal techniques, with consequent increased clarity and rigour. But it is concluded that the general theories for diagnosis must be broadened to fully encompass the problems of cognitive diagnosis.     

基于改进免疫遗传算法的电网故障诊断研究

针对电网故障的特点,应用一种改进的免疫遗传算法对电网故障进行研究。该算法能够较好地解决传统遗传算法的不足。通过引入新的交叉和变异率,更多考虑了种群的全局特征,采用动态自适应方式提取疫苗,避免了传统遗传算法收敛速度较慢的缺点。改进的算法本着优胜劣汰的思想,删除适值较低的抗体群,取而代之的是随机生成的部分新抗体,保持种群的多样性。建立一个新的目标函数,通过对一个电网的分析,验证了方法的有效性。