发电厂直流系统接地故障检测方法

介绍用于直流系统维护和故障处理的各种检测方法。     

数控机床手轮进给故障的排故思路

在数控机床加工过程中,由于调整及慢速对刀的需要,通常需要频繁地使用手轮（即手摇脉冲发生器）来控制数控机床伺服轴的运动,因此,在数控机床的诸多故障中,手轮出现故障的次数较多,同时对该故障维修人员的维修水平要求也相对要高。本文以配备有FANuc 0i—mate-TD数控系统的cK0625数控车床为例,系统地介绍了数控机床手轮故障的诊断与分析方法。     

重症监护病房紧急状况预警算法

重症监护病房中的病人身体状况通常很不稳定,常出现各种需要医护人员介入治疗的紧急状况。由于医疗资源有限,医护人员可能无法及时发现并处理这些紧急状况,给病人的存活率带来严重的负面影响。如果可以预测这些紧急状况的发生,并及时通知相关医护人员,将大大提高病人的存活率。常见重症监护病房紧急状况包括突然死亡、败血症、肺部感染、急性低血压、以及器官衰竭等。紧急状况预警建模主要采用病人的长时间生命体征监测数据,预测在一定时间之后发生某种紧急状况的可能性。预警模型所采用的监测数据分为静态数据、事件数据和时间序列数据等三类。静态数据具有容易采集、但预测准确性偏低的特点。事件数据或时间序列数据、以及多种类型数据的混合数据对于紧急状况预警模型的预测性能的提高有重要作用,将会获得更广泛的应用。     

完备ARRs集的生成方法及传感器优化配置应用

广泛应用于故障诊断和传感器优化、分析、证实的解析冗余关系(Analytical redundancy relations,ARRs)缺乏系统、有效的方法来产生完备ARRs集,为此,提出了一种逐次消元法。该方法以系统元关系(Primary relations,PRs)为基础,通过若干次循环消元过程,生成了完备ARRs集,同时生成了对应的假定特征矩阵(Hypothetical signature matrix,HSM);基于HSM,把传感器优化配置问题映射为一个特殊的0-1整数规划模型,并用分支定界法求解该模型。应用表明,该方法能在不降低故障检测率、隔离率的前提下减少传感器数目,降低了测试代价,对故障诊断中的传感器配置问题有借鉴意义。     

基于多学科协同的某锁机构系统故障仿真

为解决机电液耦合的复杂运动机构系统的故障仿真问题,提出一种基于多学科软件协同的故障仿真分析方法.通过软件接口建立机构系统基于多学科协同的仿真模型.将相关故障模式影响因素注入机构系统仿真模型,仿真分析其对系统参数的影响.某锁机构系统的上锁故障仿真分析表明,该方法对工程实际复杂运动机构系统有效可行.该故障仿真方法有助于提高机械产品可靠性,也可为复杂机械系统的故障机理分析、故障诊断和维修等提供数据支持。     

医院门诊部局域网和设计实现

分析医院门诊部局域网和实现的功能,并讲解基于ASP实现病人档案数据调取和交换。     

观测永久丢失下随机离散事件系统故障预测的验证算法

针对随机离散事件系统在故障预测时可能出现系统观测永久丢失,导致预测不准确的问题,提出一种观测永久丢失下故障预测验证的算法。首先对观测永久丢失的随机离散事件系统的U-可预测性进行了形式化。其次使用随机预测器构造了一个随机离散事件系统的U-预测器,实现了系统的故障预测。基于U-预测器,提出了随机离散事件系统U-可预测性的充分必要条件及验证算法,并且引入成对的方式,明显地改进了该验证算法的复杂度。仿真结果表明,该验证算法使得观测永久丢失下系统故障预测准确。最后,实例说明观测永久丢失下故障预测验证算法的应用。结果表明,该验证算法相比现有同类验证算法应用范围更广,验证结果更精确。     

Fault diagnosis in reconfigurable VLSI and WSI processor arrays

A systematic efficient fault diagnosis method for reconfigurable VLSI/WSI array architectures is presented. The basic idea is to utilize the output data path independence among a subset of processing elements (PEs) based on the topology of the array under test. The divide and conquer technique is applied to reduce the complexity of test application and enhance the controllability and observability of a processor array. The array under test is divided into nonoverlapping diagnosis blocks. Those PEs in the same diagnosis block can be diagnosed concurrently. The problem of finding diagnosis blocks is shown equivalent to a generalizedEight Queens problem. Three types of PEs and one type of switches, which are designed to be easily testable and reconfigurable, are used to show how to apply this approach. The main contribution of this paper is an efficient switch and link testing procedure, and a novel PE fault diagnosis approach which can speed up the testing by at leastO(V^1/2) for the processor arrays considered in this paper, where V is the number of PEs. The significance of our approach is the ability to detect as well as to locate multiple PE, switch, and link faults with little or no hardware overhead.     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

A novel method for the classification of Alzheimer's disease from normal controls using magnetic resonance imaging

Alzheimer's disease (AD) is the most prevalent form of dementia. Although fewer people, who suffer from AD are correctly and promptly diagnosed, due to a lack of knowledge of its cause and unavailability of treatment, AD is more manageable if the symptoms of mild cognitive impairment (MCI) are in an early stage. In recent years, computer‐aided diagnosis has been widely used for the diagnosis of AD. The main motive of this paper is to improve the classification and prediction accuracy of AD. In this paper, a novel approach is developed to classify MCI, normal control (NC), and AD using structural magnetic resonance imaging (sMRI) from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset (50 AD, 50 NC, 50 MCI subjects). FreeSurfer is used to process these MRI data and obtain cortical features such as volume, surface area, thickness, white matter (WM), and intrinsic curvature of the brain regions. These features are modified by normalizing each cortical region's features using the absolute maximum value of that region's features from all subjects in each group of MCI, NC, and AD independently. A total of 420 features are obtained. To address the curse of dimensionality, the obtained features are reduced to 30 features using a sequential feature selection technique. Three classifiers, namely the twin support vector machine (TSVM), least squares TSVM (LSTSVM), and robust energy‐based least squares TSVM (RELS‐TSVM), are used to evaluate the classification accuracy from the obtained features. Five‐fold and 10‐fold cross‐validation are used to validate the proposed method. Experimental results show an accuracy of 100% for the studied database. The proposed approach is innovative due to its higher classification accuracy compared to methods in the existing literature.