Fault diagnosis method of hydraulic system based on fusion of neural network and D-S evidence theory

According to fault type diversity and fault information uncertainty problem of the hydraulic driven rocket launcher servo system（HDRLSS） , the fault diagnosis method based on the evidence theory and neural network ensemble is proposed. In order to overcome the shortcomings of the single neural network, two improved neural network models are set up at the com-mon nodes to simplify the network structure. The initial fault diagnosis is based on the iron spectrum data and the pressure, flow and temperature（PFT） characteristic parameters as the input vectors of the two improved neural network models, and the diagnosis result is taken as the basic probability distribution of the evidence theory. Then the objectivity of assignment is real-ized. The initial diagnosis results of two improved neural networks are fused by D-S evidence theory. The experimental results show that this method can avoid the misdiagnosis of neural network recognition and improve the accuracy of the fault diagnosis of HDRLSS.

Enhancing Evidence-Based Practice and Critical Thinking Skills of Nursing Students: Lessons from the Review of the Bachelor of Science in Nursing Curriculum of the University of Zambia

Background: Making changes in the existing curriculum aims, objectives, course contents learning outcomes and assessment strategies have become a fact of life for nurse educators. Purpose: To enhance the existing Bachelor of Science (BSc) in nursing curriculum through integration of evidence-based practice (EBP) and teaching of critical thinking skills. Materials and Methods: A needs analysis was conducted using a five-phased approach to review the BSc in nursing Curriculum. Kern’s six-step model was adapted and introduced through a series of workshop exercises that highlighted the application of each step: 1) Desk review of the BSc curriculum offered globally;2) Administration of the needs assessment questionnaire to key informants;3) Strengths, weakness, opportunities and threat analysis;4) Consultative meeting with major stakeholders;5) Curriculum review. Results: The five-phased approach established some gaps in existing curricula, and identified critical core competences and best practices in integrating EPB and critical thinking in the BSc undergraduate curriculum and some “A” level content that was not in tandem with the practice of nurses. New courses were developed to support students in academic writing and enhance professionalism and duration of training was reduced from 5 to 4 years. Conclusion: The process demonstrated that BSc curriculum review, in fact, should be thoroughly scrutinized to encourage positive changes to the curriculum, provide opportunities for team building and the development of leadership skills and a whole-of course perspective on the curriculum.

Study on Power Transformers Fault Diagnosis Based on Wavelet Neural Network and D-S Evidence Theory

>Transformer faults are quite complicated phenomena and can occur due to a variety of reasons.There have been several methods for transformer fault synthetic diagnosis,but each of them has its own limitations in real fault diagnosis applications.In order to overcome those shortcomings in the existing methods,a new transformer fault diagnosis method based on a wavelet neural network optimized by adaptive genetic algorithm(AGA)and an improved D-S evidence theory fusion technique is proposed in this paper.The proposed method combines the oil chromatogram data and the off-line electrical test data of transformers to carry out fault diagnosis.Based on the fusion mechanism of D-S evidence theory,the comprehensive reliability of evidence is constructed by considering the evidence importance,the outputs of the neural network and the expert experience.The new method increases the objectivity of the basic probability assignment(BPA)and reduces the basic probability assigned for uncertain and unimportant information.The case study results of using the proposed method show that it has a good performance of fault diagnosis for transformers.

Validity of Diagnostic Evidence for Deceased Cases in Hospitals

Objective To determine the validity of the diagnostic evidence for deceased cases in hospitals. Methods All information collected from medical records of the deceased cases in tertiary care health facilities was input into ottr database. Four diagnosis levels were determined based on level of diagnostic evidence： level Ⅰ was based on autopsy, pathology or operative exploration, level Ⅱ on physical and laboratory tests plus expert clinical judgment, level Ⅲ on expert clinical judgment, level Ⅳ on postmortem assumptions. After the diagnostic evidence of each deceased case was reviewed by a panel of three experts, the diagnostic level of each diagnosis was determined. Results Among the 2102 medical cases for verbal autopsy study, only 26 （1.24%） afforded diagnostic evidence for level Ⅲ Among the level Ⅲ evidence-based cases of death, the major causes of death were cardiovascular diseases, respiratory diseases, and gastroenterological diseases. According to some special symptoms and medical histories, these cases could be diagnosed by comprehensive clinical judgment. Only one case met the criteria for level Ⅳ. Conclusion Level Ⅰ diagnostic evidence is hard to attain in China because of the traditional concept and economic restriction. The causes for 2101 deaths can be validated by level Ⅱ or Ⅲ diagnostic evidence.

Inquiry Based on Evidence and its Practices in Two Healthcare Facilities of Bujumbura

Background: Inquiry evidence-based practice (IBP) improves healthcare quality, reliability, and patient outcomes as well as reduces variations in care and costs. IBP and its practice in health care promote also many advantages, such as improvements of practices based on the attitudes and cognitive ideas. This study aims to assess the inquiry based on evidence (IBP) and its practices in two Health Care Facilities (HCFs) of Bujumbura to help the practitioners to understand its importance. Methods: A cross-sectional study design was used to analyze the importance of IBP and its practice in these two hospitals. The probability-sampling technique was used also to select 104 nurses from the Military Hospital of Kamenge and 55 nurses from the Van Norman Clinic. A questionnaire was used to collect data with two mains components, demographic data and knowledge and attitudes addressing the following parameters: evidence practice during inquiry, nursing theory, current analysis in nursing care oriented the evidence, prioritization of care, rational diagnostic, monitoring and assessment. Results: The findings from this study revealed a poor knowledge and attitude among participants towards Inquiry Based Practice. In all variables, participants were scoring less than 10%. However, majority of participants (76.5%) know the indicators of patients’ satisfaction with nursing interventions through survey-based practice and 74.1% argued to analyze their information collected. Conclusion: This study revealed a weak awareness on IBP and its importance during nursing practice among participants as for almost all variables, participants were scoring less than 10%, except for the indicators of patients’ satisfaction with nursing interventions through survey-based practice (76.5%). Therefore, in-service training and curriculum revision had been highlighted and recommended another to provide the best rational diagnosis and achieve the patient’s outcomes.

A new luciferase immunoprecipitation system assay provided serological evidence for missed diagnosis of severe fever with thrombocytopenia syndrome

Severe fever with thrombocytopenia syndrome(SFTS),caused by SFTS virus(SFTSV)infection,was first reported in 2010 in China with an initial fatality of up to 30%.The laboratory confirmation of SFTSV infection in terms of detection of viral RNA or antibody levels is critical for SFTS diagnosis and therapy.In this study,a new luciferase immunoprecipitation system(LIPS)assay based on p REN2 plasmid expressing SFTSV NP gene and tagged with Renilla luciferase(Rluc),was established and used to investigate the levels of antibody responses to SFTSV.Totally 464 serum samples from febrile patients were collected in the hospital of Shaoxing City in Zhejiang Province in 2019.The results showed that 82 of the 464 patients(17.7%)had antibody response to SFTSV,which were further supported by immunofluorescence assays(IFAs).Further,q RT-PCR and microneutralization tests showed that among the 82 positive cases,15 patients had viremia,10 patients had neutralizing antibody,and one had both(totally 26 patient).However,none of these patients were diagnosed as SFTS in the hospital probably because of their mild symptoms or subclinical manifestations.All the results indicated that at least the 26 patients having viremia or neutralizing antibody were the missed diagnosis of SFTS cases.The findings suggested the occurrence of SFTS and the SFTS incidence were higher than the reported level in Shaoxing in 2019,and that LIPS may provide an alternative strategy to confirm SFTSV infection in the laboratory.

An equine disease diagnosis expert system based on improved reasoning of evidence credibility

In China,there is a troubling shortage of well-trained equine veterinarians,leaving the needs of many equine farmers unmet.This is especially true with respect to the diagnosis of equine diseases.To solve this shortcoming,an equine disease diagnosis expert system was developed.For the aspect of knowledge representation,the structure of equine disease diagnosis knowledge was analyzed using an ontology system.Next,the clinical signs were described using an object-attribute-value(O-A-V)format,and the knowledge representation was then expressed using production rules.With respect to the reasoning mechanism,the weights of the clinical signs and promoted confidence factors(PCF)were combined to express information and rules pertaining to clinical signs with an associated level of uncertainty.The model was established based on improved reasoning of evidence credibility.Finally,using the ASP.Net platform and the SQL Server 2008 database,the equine disease diagnosis expert system based on the B/S structure has been developed,and is capable of reliably diagnosing 40 of the most common equine diseases.A functional evaluation of the system was conducted,and the diagnostic accuracy was observed to be 88%.This study demonstrates a bright prospect for the popularization and application of the system through continuous system maintenance and knowledge-based updates.

Dynamic Fault Diagnosis Using the Improved Linear Evidence Updating Strategy

The majority of the existing fault diagnosis methods using Dempster-Shafer(DS) evidence theory(DST) all provide the "static" fused results by combining several pieces of diagnosis evidence, which only reflect the current running status of monitored equipment. This paper presents the dynamic diagnosis strategy by using recursively the improved linear evidence updating rule. Its updated result can synthesize the diagnosis evidence collected at historical, current and future time steps by dynamically adjusting the proposed smoothing linear combination weights. The diagnosis examples of machine rotor show that the proposed method can provide more reliable and accurate results than the diagnosis methods based on the classical updating strategies.

INTELLIGENT FUSION FOR AEROENGINE WEAR FAULT DIAGNOSIS

Four common oil analysis techniques, including the ferrography analysis （FA）, the spectrometric oil analysis （SOA）, the particle count analysis （PCA）, and the oil quality testing （OQT）, are used to implement the military aeroengine wear fault diagnosis during the test drive process. To improve the precision and the reliability of the diagnosis, the aeroengine wear fault fusion diagnosis method based on the neural networks （NN） and the Dempster-Shafter （D-S） evidence theory is proposed. Firstly, according to the standard value of the wear limit, original data are pre-processed into Boolean values. Secondly, sub-NNs are established to perform the single diagnosis, and their training samples are dependent on experiences from experts. After each sub-NN is trained, diagnosis results are obtained. Thirdly, the diagnosis results of each sub-NN are considered as the basic probability allocation value to faults. The improved D-S evidence theory is applied to the fusion diagnosis, and the final fusion results are obtained. Finally, the method is verified by a diagnosis example.

Application of data fusion method to fault diagnosis of nuclear power plant

The work condition of nuclear power plant (NPP) is very bad, which makes ithas faults easily. In order to diagnose (he faults real time, the fusion diagnosis system is built.The data fusion fault diagnosis system adopts data fusion method and divides the fault diagnosisinto three levels, which are data fusion level, feature level and decision level. The feature leveluses three parallel neural networks whose structures are the same. The purpose of using neuralnetworks is mainly to get basic probability assignment ( BPA) of D-S evidence theory, and the neuralnetworks in feature level are used for local diagnosis. D-S evidence theory is adopted to integratethe local diagnosis results in decision level. The reactor coolant system is the study object andwe choose 2# steam generator U-tubes break of the reactor coolant system as a diagnostic example.The experiments prove that the fusion diagnosis system can satisfy the fault diagnosis requirementof complicated system, and verify that the fusion fault diagnosis system can realize the faultdiagnosis of NPP on line timely.

Application of Multi-sensor Information Fusion in the Fault Diagnosis of Hydraulic System

Aiming at the problem of incomplete information and uncertainties in the diagnosis of complex system by using single parameter, a new method of multi-sensor information fusion fault diagnosis based on BP neural network and D-S evidence theory is proposed. In order to simplify the structure of BP neural network, two parallel BP neural networks are used to diagnose the fault data at first; and then, using the evidence theory to fuse the local diagnostic results, the accurate inference of the inaccurate information is realized, and the accurate diagnosis resuh is obtained. The method is applied to the fault diagnosis of the hydraulic driven servo system （HDSS） in a certain type of rocket launcher, which realizes the fault location and diagnosis of the main components of the hydraulic driven servo system, and effectively improves the reliability of the system.

Application of the fault diagnosis strategy based on hierarchical information fusion in motors fault diagnosis

This paper has analyzed merits and demerits of both neural network technique and of the information fusion methods based on the D-S (dempster-shafer evidence) Theory as well as their complementarity, proposed the hierarchical information fusion fault diagnosis strategy by combining the neural network technique and the fused decision diagnosis based on D-S Theory, and established a corresponding functional model. Thus, we can not only solve a series of problems caused by rapid growth in size and complexity of neural network structure with diagnosis parameters increasing, but also can provide effective method for basic probability assignment in D-S Theory. The application of the strategy to diagnosing faults of motor bearings has proved that this method is of fairly high accuracy and reliability in fault diagnosis.

从数据到证据:面向循证教学的学情诊断方法

在循证教学领域,证据的可靠性是确保教学改进措施科学性和有效性的重要前提。文章针对循证教学中证据的可靠性问题,提出了面向循证教学的学情诊断方法,能提炼可靠证据并应用于循证教学。此方法基于大规模的学生学习数据,运用随机森林对数据的特征重要性进行评估,增强了数据选取的可信度;同时将随机森林与AdaBoost融合成随机森林-AdaBoost算法,用于学生期末综合评价成绩层级的学情诊断,提升了诊断结果的可靠性。为验证此方法的应用效果,文章通过对比实验进行了实证研究,结果显示:该方法不仅具有较高的预测准确度,而且表现出良好的稳定性和鲁棒性。文章从繁杂的学习数据中提炼出有效证据,提高了学情诊断的准确性,为实施个性化教学提供了依据,并有助于推动循证教学向数字化和智能化方向发展。

2型糖尿病中西医结合诊疗指南

2型糖尿病(T2DM)是全球性的公共卫生问题,目前我国患病总人数已超过1亿。为指导2型糖尿病规范化中西医诊疗,中国中西医结合学会联合中华中医药学会、中华医学会共同开展中西医结合诊疗方案制订工作,本指南组织国内22位内分泌及相关领域中西医专家,通过前期调研、专家访谈、共识会议,结合循证医学证据,最终形成了2型糖尿病中西医结合诊疗指南。主要内容包括2型糖尿病的诊断标准、高危人群筛查、中医证候分型、治疗原则和方法。治疗方案涵盖健康教育与管理、营养与药膳、运动与功法、心理调节与情志疗法,以及中药辨证论治、中成药、中医外治法等中医疗法与西药的联合干预。强调以患者为中心,注重个体化治疗,旨在改善血糖控制,减少并发症,提高生活质量。本指南适用于中医、中西医结合医疗机构的临床医师及相关护理人员,也可供西医临床医师和健康管理相关专业人员参考。

基于CD-BSMOTE的D-S证据融合变压器故障诊断

针对变压器油中溶解气体数据集不均衡特性对故障诊断结果的影响,提出一种基于清除临界点改进的边界合成少数类过采样算法均衡数据集和Pearson冲突距离改进D-S证据融合的变压器故障诊断模型。首先,对少数类样本进行均衡化处理,根据K-means聚类结果清除处于临界位置的样本;其次,搭建梯度提升树、随机森林、BP神经网络的故障诊断模型,实现变压器故障初步诊断;接着引入Pearson冲突距离改进D-S证据融合模型,实现诊断结果的融合决策;最后,经实际算例分析,诊断精确率达到92.65%。结果表明,所建模型能有效解决数据不平衡对诊断结果的影响,提高故障诊断精度。

基于加权D-S证据理论的旋翼故障诊断

旋翼作为直升机的升力面和操作面,其健康状态对直升机的安全至关重要。旋翼故障诊断技术仍是直升机健康与使用监测系统(Health and usage monitoring system, HUMS)领域的薄弱环节,开发旋翼故障诊断技术具有重要价值。基于信息融合技术,首先分析了旋翼故障的诊断机理,建立了旋翼故障模型,通过流固耦合仿真获取了不同故障下桨叶、轮毂和机身的故障特征信息,生成数据集进行网络训练和验证。然后,利用遗传算法反向传播(Genetic algorithm-backpropagation, GA-BP)优化神经网络诊断3种类型的直升机旋翼故障,即后缘调整片误调、变距拉杆误调和桨叶质量不平衡。3个逐级神经网络分别对旋翼故障类型、故障位置和故障程度进行了诊断识别。最后采用加权的Dempster-Shafer(D-S)证据理论对旋翼故障进行诊断和分析。结果证明基于改进D-S证据理论的旋翼故障诊断方法能够成功应用到旋翼故障诊断中,并具有良好的识别效果。

循证课堂诊断:价值意蕴、要素结构与行动逻辑

课堂诊断是提高课堂教学有效性的一种重要手段。审视传统课堂诊断发现,诊断标准易受个体主观经验所桎梏、诊断主体力量构成缺乏多元化、诊断过程中归纳意识缺失等,这是传统课堂诊断实践的局限所在。循证实践是耦合课堂诊断内涵、要素和发展的分析视角,循证课堂诊断具有提高教师循证意识与能力、优化教师教学行为、促进实践性知识转化生成的实践价值;循证课堂诊断由“研究共同体”“诊断证据”与“课堂情境”三个核心要素构成;课堂教学切片诊断是典型的循证课堂诊断,其行动逻辑主要分为四个阶段:观察课例,定性问题;提炼经验,归纳证据;依循证据,诊断课堂;更新证据,创生知识。

决策级融合轴承故障混合智能诊断

基于机器学习算法的智能诊断方法在轴承故障诊断中应用广泛,但是大多数诊断模型均基于某一种机器学习算法。不同的机器学习算法具有不同的特点和适用范围,基于单一算法的智能诊断模型提取故障信息的能力存在一定的局限性,容易导致误诊和漏报。针对该问题,提出一种轴承故障混合智能诊断模型。该模型的详细技术路线如下:首先,提取对轴承故障敏感的时域和频域特征来构造特征集;其次,利用MLP神经网络、多分类SVM和随机森林三种智能分类器对轴承故障进行初步诊断;最后,利用D-S证据理论进行决策级融合,以综合利用多种机器学习算法的优点和适应性,并输出最终的判决结果。实验表明该模型具有内部纠正机制,较之单分类器诊断模型准确率明显提升,更加适用于轴承的故障诊断。

基于二维CNN与多源机电信息融合的同步电机转子匝间短路故障诊断方法

为提高同步电机转子绕组匝间短路故障的诊断准确率,以一台型号为SDF-9的一对极同步发电机为研究对象,提出了一种基于二维卷积神经网络(Convolutional Neural Networks, CNN)与多源机电信息融合的匝间短路故障诊断方法。首先选取故障前后的定子环流、转子振动、定子振动信号为故障特征,采用信号-图像转换方法将一维时序信号转化为二维灰度图像。其次将处理后的图像分别作为二维CNN模型的前置输入进行训练。最后采用D-S证据理论将3种证据体的输出概率进行决策融合。结果表明:该方法消除了单一信号易受传感器故障及环境变化的影响,故障诊断准确率显著提高,并与其他传统故障诊断算法的诊断结果进行对比分析,验证了此方法的有效性。

基于多传感器信息融合的水工闸门故障诊断

针对水工闸门安全检修困难、检修效率低的问题,提出了一种基于支持向量机(Support vector machine,SVM)和改进D-S证据理论的信息融合水工闸门故障诊断方法。该方法通过提取不同传感器诊断信号小波包信息熵特征构建特征子空间,然后在每个特征子空间构建诊断子网络,最后使用改进证据理论对每个诊断子网络的输入进行决策层融合,从而水工闸门的多信息融合诊断结果。闸门故障诊断实验结果显示,信息融合的闸门故障诊断方法可有效识别弧形闸门故障种类,其故障诊断准确率达到了98.33%,同时诊断可靠度高,各类故障的诊断不确定度均小于1%。实验结果验证了智能故障诊断方法用于水工闸门领域的可行性,对于改进水工闸门故障检修方式,推动水利工程智能化的发展具有重大意义。