基于最小诊断集的贝叶斯网络诊断模型研究

为了克服基于模型的诊断方法不能确定诊断最优解的缺陷,提出以基于模型的诊断所生成的最小诊断集构建贝叶斯网络的诊断方法,它能通过贝叶斯网络确定候选诊断解的发生概率,进而确定系统最有可能的诊断解。并针对当最小诊断解较多时,构建的贝叶斯网络将非常复杂,不易诊断的问题,提出了超件的概念,并将超件应用于贝叶斯网络构建,以简化构建的贝叶斯网络,提高诊断效率。最后以某电路为例进行诊断分析。

背痛诊断集团

Electrophoretics是一家研究慢性脊背疼痛的诊断公司,它以约2600万英磅的资金打入股票市场。该集团以伦敦为基地,同时与美国和瑞士有广泛的业务联系,目前已经鉴定出一种血液蛋白,该蛋白可能与引起慢性脊柱疼痛的神经损伤相关联。Electrophoretics已申请了一项关于诊断检验品的专利,估计可在2年内投放市场。该公司计划寻找已建立的医药和诊断公司。

基于融合策略的集成化智能诊断系统研究

文章比较系统的研究了目前在复杂电子设备诊断领域应用广泛的几种诊断方法 ,分析了各方法的优缺点和适用领域 ,并在此基础上 ,提出了基于融合策略的集成诊断构想 。

一种基于ICQPSO的FCM模拟电路可诊断元件集识别方法

在模拟电路模糊组识别过程中,模糊C均值聚类(FCM,fuzzy C-means clustering algorithm)易陷入局部最优。针对此种缺陷,提出一种基于ICQPSO的FCM模拟电路可诊断元件集识别方法。首先对ICQPSO算法进行了分析;然后介绍了FCM算法及其有效性指标,在此基础上研究了基于ICQPSO的FCM方法;最后以某模拟电路为例进行了仿真实验。仿真结果表明,将该方法应用于模拟电路可诊断元件集的识别是行之有效的。

利用结构信息的故障诊断方法

基于模型的故障诊断方法是重要的故障诊断方法之一,该方法主要的问题就是如何求得所有的诊断.该文利用系统的结构信息,给出了求极小冲突集的一个算法,证明了算法的正确性,分析了算法的复杂性;然后介绍了如何由极小冲突求得诊断.其次,还给出了利用结构信息直接求诊断的一个算法,证明了其正确性.最后与相关工作进行了比较.该文给出的算法,对于一些特殊结构的系统可在多项式时间内结束.

基于逻辑的一种诊断方法

基于逻辑的故障诊断方法中一个没有解决好的问题就是如何求得所有极小冲突集。讨论了如何由极小不协调子集求得极小冲突集 ,然后给出了求所有极小不协调子集的一种方法。对如何应用该算法求得所有的极小冲突集 ,给出了求诊断的一个过程 ,最后与相关工作进行了比较。

集控站数据同步与故障诊断专家系统

针对现有集控站系统双机数据存储、更新不同步和故障排除困难等问题,提出了在自动化主站建设集控站数据同步与故障诊断专家系统必要性和关键技术。集控站监视控制和数据采集(SCADA)系统具有数据同步与故障诊断系统故障专家诊断和自恢复功能,避免因为数据存储、更新不同步而引发的操作事故,保证了电网及设备的安全运行。

基于动态聚类的有限状态机多错误诊断

为了解决多错误诊断时枚举数量过大的问题,提出一种基于动态聚类分析的方法.首先,按照是否具有相同的初始症状冲突集对失败用例进行聚类,并计算初始症状冲突集及其转换的可疑度;然后,按照可疑度的大小枚举可能发生错误的转换组合,在枚举过程中进行再次聚类;最后,用测试集验证错误可能,生成错误诊断集.实验结果表明,该方法可以有效减少错误枚举数量,提高诊断效率.

新能源汽车维修保养技术分析

相较于传统燃油汽车,新能源汽车技术更为复杂,涉及高压电气系统、大容量动力电池等专业知识,在维修保养方面难度较大,安全风险更高,如果缺乏系统的维修保养技术知识作为支撑,将制约新能源汽车的推广应用。该文阐述新能源汽车维修保养的意义,分析新能源汽车维修保养关键技术,并展望新能源汽车维修保养技术的发展趋势,为新能源汽车大规模商用提供技术支撑。

一次回风空调系统的故障诊断研究

空调系统的故障诊断研究对于节约空调系统能耗、减少设备之间的磨损、延长设备的使用寿命、提高空调系统的运行效率均具有重要的意义。由于空调系统的复杂性、空调设备的多样性,空调系统在实际运行过程中会存在多种多样的问题,包括硬件设备的老化、阀门的堵塞、传感器失灵、换热器换热效率降低等,都会造成能源消耗增加、能源浪费等问题。因此针对空调系统的特点研究一套适用于该系统的故障诊断方法,实现对系统运行过程中常见故障的诊断,主要针对组合式空气处理机组。通过实现模拟再现常见的7种故障,利用一阶自回归方程建立特征参数残差图,由残差分析得到不同故障条件下特征参数的残差变化,进一步针对残差变化趋势以送分温度残差为基准建立故障诊断分类器,并细化为7条故障诊断规则集;同时,通过Matlab Script节点实现Matlab与LabVIEW的混合编程,并调用训练好的模糊神经网络模型实现故障诊断结果的可视化显示。

Fault Diagnosis of Vehicle Transmission System Based on Rough Set Theory

Rough set theory is used to treat the data of vehicle transmission system faults. The minimum fault feature vector can be obtained by calculating the importance and dependency of each attribute. Real time diagnosis, as a result, can be actualized. Ultimate decision making can be done by analyzing the consistency of decision information. The result shows that rough set theory is useful and possesses its unique merits in this field.

Hologamous integrating strategies for incipient fault diagnosis of transformer

This paper studied an integrative fault diagnostic system on the power transformer. On-line monitor items were grounded current of iron core, internal partial discharge and oil dissolved gas. Diagnostic techniques were simple rule-based judgment, fuzzy logistic reasoning and neural network distinguishing. Considering that much faults information was interactional, intellectualized diagnosis was implemented based on integrating the neural network with the expert system. Hologamous integrating strategies were materialized by information-based integrating monitor devices, shared information database on several levels and fusion diagnosis software along thought patterns. The expert system practiced logic thought by logistic reasoning. The neural network realized image thought by model matching. Creative conclusion was educed by their integrating. The diagnosis example showed that the integrative diagnostic system was reasonable and practical.

“深圳版”疾病诊断术语集的制作和应用研究

深圳市作为全国首批应用C-DRG开展收付费改革的试点地区之一,当前改革工作已稳步推进。通过在《中国临床疾病诊断规范术语集》基础上,结合深圳实际情况,制作《深圳市临床疾病诊断规范术语集》,并以此作为深圳地区统一的临床疾病诊断规范术语库。其不仅支持深圳市C-DRG应用,同时成功实现了DRG分组、医保付费及各级卫生健康行政部门及医院管理者对临床进行标准化管理等功能,满足了新时代更高的卫生改革要求。

Dynamic change and diagnosis of physical, chemical and biological properties in bauxite residue disposal areas

Vegetation encroachment occurred in bauxite residue disposal area(BRDA)following natural weathering processes,whilst the typical indicators of soil formation are still uncertain.Residue samples were collected from the BRDA in Central China,and related physical,chemical and biological indicators of bauxite residue with different storage years were determined.The indicators of soil formation in bauxite residue were selected using principal component analysis,factor analysis,and comprehensive evaluation to establish soil quality diagnostic index model on disposal areas.Following natural weathering processes,the texture of bauxite residue changed from silty loam to sandy loam.The pH and EC decreased,whilst porosity,nutrient element content and microbial biomass increased.The identified minimum data set(MDS)included available phosphorus(AP),moisture content(MC),C/N,sand content,total nitrogen(TN),microbial biomass carbon(MBC),and pH.The soil quality index of bauxite residue increased,and the relative soil quality index decreased from 1.89 to 0.15,which indicated that natural weathering had a significant effect on improveing the quality of bauxite residue and forming a new soil-like matrix.The diagnostic model of bauxite residue was established to provide data support for the regeneration on disposal area.

《中国临床疾病诊断规范术语集》的临床应用与实践

《中国临床疾病诊断规范术语集》作为C-DRG体系中的重要分组工具之一,由全国37个专业735名国家级临床专家结合中国临床教学和临床应用的实践制定而成。术语集一病一码,每一条诊断术语均与《2016版疾病分类与代码》(GB/T 14396-2016)每一个词条进行了对接,以确保试点地区选用的诊断术语在病案统计上对应的是一条ICD-10疾病分类词条。本术语集中增入了罕见病相关术语,最大化覆盖临床全诊断。作为DRG分组的重要基础工具之一,《中国临床疾病诊断规范术语集》应当由临床医生在电子病历中进行勾选,配合信息系统改造使用。在医院应用术语集有助于医院之间信息标准的统一、信息流程的优化,从而加强医院的精细化管理。

Fault Diagnosis Approach of Local Ventilation System in Coal Mines Based on Multidisciplinary Technology

In order to reduce the probability of fault occurrence of local ventilation system in coal mine and prevent gas from exceeding the standard limit, an approach incorporating the reliability analysis, rough set theory, genetic algorithm (GA), and intelligent decision support system (IDSS) was used to establish and develop a fault diagnosis system of local ventilation in coal mine. Fault tree model was established and its reliability analysis was performed. The algorithms and software of key fault symptom and fault diagnosis rule acquiring were also analyzed and developed. Finally, a prototype system was developed and demonstrated by a mine instance. The research results indicate that the proposed approach in this paper can accurately and quickly find the fault reason in a local ventilation system of coal mines and can reduce difficulty of the fault diagnosis of the local ventilation system, which is significant to decrease gas exploding accidents in coal mines.

Application of empirical mode decomposition in early diagnosis of magnetic memory signal

In order to eliminate noise interference of metal magnetic memory signal in early diagnosis of stress concentration zones and metal defects, the empirical mode decomposition method combined with the magnetic field gradient characteristic was proposed. A compressive force periodically acting upon a casing pipe led to appreciable deformation, and magnetic signals were measured by a magnetic indicator TSC-1M-4. The raw magnetic memory signal was first decomposed into different intrinsic mode functions and a residue, and the magnetic field gradient distribution of the subsequent reconstructed signal was obtained. The experimental results show that the gradient around 350 mm represents the maximum value ignoring the marginal effect, and there is a good correlation between the real maximum field gradient and the stress concentration zone. The wavelet transform associated with envelop analysis also exhibits this gradient characteristic, indicating that the proposed method is effective for early identifying critical zones.

Feasibility and Diagnostic Accuracy for Assessment of Coronary Artery Stenosis of Prospectively Electrocardiogram-gated High-pitch Spiral Acquisition Mode Dual-source CT Coronary Angiography in Patients with Relatively Higher Heart Rates: in Comparison wit

Objective To prospectively investigate the diagnostic accuracy for coronary artery stenosis of prospectively electrocardiogram-triggered spiral acquisition mode (high pitch mode) dual-source computed tomography coronary angiography (CTCA) in patients with relatively higher heart rates (HR) compared with catheter coronary angiography (CCA). Methods Forty-seven consecutive patients with relatively higher HR (>65 and <100 bpm) (20 male, 27 female; age 55±10 years) who both underwent dual-source CTCA and CCA were prospectively included in this study. All patients were performed CTCA using high pitch mode setting at 20%-30% of the R-R interval for the image acquisition. All coronary segments were evaluated by two blinded and independent observers with regard to image quality on a three-point scale (1: excellent to 3: non-diagnostic) and for the presence of significant coronary stenoses (defined as diameter narrowing exceeding 50%). Considered CCA as the standard of reference, the sensitivity, specificity, positive predictive value and negative predictive value were calculated. Radiation dose values were calculated using the dose-length product. Results Image quality was rated as being score 1 in 92.4% of segments, score 2 in 6.1% of segmentsand score 3 in 1.5% of segments. The average image quality score per segment was 1.064±0.306. The HR variability of patients with image score 1, 2 and 3 were 2.29±1.06 bpm, 5.17±1.37 bpm, 8.88±1.53 bpm, respectively. The average HR variability of patients with different image scores were significantly different (F=170.402, P=0.001). The sensitivity, specificity, positive and negative predictive values were 92.6%, 97.0%, 87.6%, 98.3%, respectively, per segment and 90.0%, 95.2%, 85.3%, 96.9%, respectively, per vessel and 100%, 63.6%, 90.0%, 100%, respectively, per patient. The effective radiation dose was on average 0.86±0.16 mSv. Conclusion In patients with HR more than 65 bpm and below 100 bpm without cardiac arrhythmia, the prospectively electrocardiogram-gated high-pitch spiral acquisition mode with image acquired timing set at 20%-30% of the R-R interval provides a high diagnostic accuracy for the assessment of coronary stenoses combined with a 1.5% of non-diagnostic coronary segments and a radiation dose below 1 mSv.

Multi Boost with ENN-based ensemble fault diagnosis method and its application in complicated chemical process

Fault diagnosis plays an important role in complicated industrial process.It is a challenging task to detect,identify and locate faults quickly and accurately for large-scale process system.To solve the problem,a novel Multi Boost-based integrated ENN(extension neural network) fault diagnosis method is proposed.Fault data of complicated chemical process have some difficult-to-handle characteristics,such as high-dimension,non-linear and non-Gaussian distribution,so we use margin discriminant projection(MDP) algorithm to reduce dimensions and extract main features.Then,the affinity propagation(AP) clustering method is used to select core data and boundary data as training samples to reduce memory consumption and shorten learning time.Afterwards,an integrated ENN classifier based on Multi Boost strategy is constructed to identify fault types.The artificial data sets are tested to verify the effectiveness of the proposed method and make a detailed sensitivity analysis for the key parameters.Finally,a real industrial system—Tennessee Eastman(TE) process is employed to evaluate the performance of the proposed method.And the results show that the proposed method is efficient and capable to diagnose various types of faults in complicated chemical process.

Teat Length and Lactation Period as A Predisposition Factor of Subclinical Mastitis in Dairy Cattle in Bandung, Indonesia

In order to improve the practical approach to clinical diagnosis of subclinical mastitis, 72 dairy cattle from KPSBU Lembang Bandung Regency was used in this research. The purpose of this research was to study the correlation between udder length and lactation period with the profile of subclinical mastitis. Lactation period data was collected from interviews with farmers. Subclinical mastitis testing by IPB-1 Mastitis Test reagent and udder length measured using measuring tape （cm）. Data was analyzed using Duncan test and logistic regression. The highest correlation of subclinical mastitis occurred to dairy cattle with average udder length of 7.5 cm and was in the third and forth lactation period. This result shows that udder length and lactation period of dairy cattle is a predisposing factor for subclinical mastitis. This finding is important to improve the milking management as well as a way to diagnose subclinical mastitis.