基于粗糙集的故障诊断特征提取

故障的特征提取对于进行准确可靠的诊断非常重要。而实际的故障诊断数据样本的分类边界常常是不确定的,并且故障与征兆之间的关系往往也是不确定的。粗糙集理论是处理模糊和不确定性问题的新的数学工具。论文将粗糙集理论引入到故障诊断特征提取,提出了一种基于粗糙集的故障诊断特征提取方法。并通过两个故障诊断实例对该方法进行了验证。结果表明:在有效地保持故障诊断分类结果的情况下,该方法可以提取出最能反映故障的特征,从而为粗糙集在故障诊断中的深入应用打下了基础。     

基于粗糙集理论的关联规则挖掘模型

提出了一个基于粗糙集理论的关联规则挖掘模型。介绍了该规则挖掘模型的主要步骤，模型中应用了属性约简和规则约简技术，并给出了该两个技术的算法。     

基于粗糙集理论的故障诊断知识获取

利用飞行数据对飞机进行故障诊断和状态监控已经成为国内外相关领域研究的热点.根据飞行数据进行故障诊断时,需要获得故障模式与飞行数据特点之间的对应关系的知识,但是这些知识很难在维修手册等知识源中获得.大量的历史飞行数据中隐含了飞行数据与故障模式之间的逻辑因果关系,对其分析处理就可以获取诊断知识.因此本文提出了应用粗糙集的方...     

粗糙集理论在故障诊断专家系统中的应用

针对专家系统中知识获取的瓶颈问题，引入了一种基于粗糙集理论的专家系统模型。该模型在知识入库前对其进行过滤，并利用粗糙集理论的约简算法消除知识库的冗余，从而实现了对知识库结构和性能的有效维护及完善。     

基于粗糙集人工神经网络的锅炉故障诊断模型

针对锅炉这种大型特种设备,提出了一种基于粗糙集和人工神经网络集成的智能故障诊断方法.该方法先利用Rs理论建立故障决策表,对原始数据进行约简,并按照一定的原则选取多个约简;然后建立神经网络故障诊断子系统,使用粗糙集处理后的数据计算出故障发生程度,研究结果表明:该方法能够正确而且高效地诊断出锅炉中各种部件的故障发生的严重程度.     

一种基于粗糙集理论的智能故障诊断新方法

论文针对基于规则的故障源分离与定位方法中的一个关键问题,即诊断规则的获取,利用粗糙集的基本原理构造出了一种用于规则提取的新方法,其中包括了用于对故障决策表,即故障字典,进行属性约简的改进算法和属性值的顺序约简算法。该方法能够迅速从故障字典中提取出诊断规则,并揭示出故障信息内在的冗余性。最后实例应用的结果表明了该方法的有效性,尤其是在不完全信息情况下的有效性。     

基于粗糙集理论的数据挖掘算法研究

本文提出一种基于粗糙集理论的数据挖掘模型，从实际数据出发，运用不同简化层次的算法，导出每个层次上的信息集，最后得到规则集，在进行推理和决策分析时，按照一定算法进行匹配得出结论。还给出了模拟例子说明如何建立和运用这种数据挖掘模型。     

基于粗糙集的电力设备故障诊断

针对当前专家系统知识获取瓶颈的难题,提出了基于粗糙集数据挖掘的电力设备故障诊断方法,首先对电力设备历史数据、基础信息数据库和缺陷信息数据库进行区分,接着简约数据并建立故障诊断决策表,采用粗糙集数据挖掘方法对在线数据进行决策判断,推断出潜在的诊断规则,这对电力设备故障预报及诊断系统的设计具有借鉴意义和深入研究的价值.     

基于粗糙集神经网络的网络故障诊断新方法

摘要针对传统网络故障知识库冗余度高和稳定性难以两全的缺陷,综合运用神经网络方法和粗糙集理论,提出了RSNN算法,实现不一致情况下的规则获取和学习样本的净化处理．该算法具有简化样本、适应性强、容错性高和不易陷入局部最小点等特点,能有效处理网络故障诊断中噪声或不相容的信息．实验表明,利用该方法实现的系统与同类的其他方法相比,提高了诊断准确率和诊断速度．     

基于粗糙集理论的神经网络研究及应用

为了补偿神经网络的黑箱特性并提高其工作性能,将粗糙集理论同神经网络结合起来,提出一种基于粗糙集的神经网络体系结构．首先,利用粗糙集理论对神经网络初始化参数的选择和确定进行指导,赋予各参数相关的物理意义;然后,以系统输出误差最小化为目标对粗糙神经网络进行训练,使其满足性能要求．实验结果表明,粗糙神经网络能较好地完成数据挖掘任务,并能获得较高的分类精度．     

Feature-based rule induction in machining operation using rough set theory for quality assurance

There have been many studies, mainly by the use of statistical modeling techniques, as to predicting quality characteristics in machining operations where a large number of process variables need to be considered. In conventional metal removal processes, however, an exact prediction of surface roughness is not possible or very difficult to achieve, due to the stochastic nature of machining processes. In this paper, a novel approach is proposed to solve the quality assurance problem in predicting the acceptance of computer numerical control (CNC) machined parts, rather than focusing on the prediction of precise surface roughness values. One of the data mining techniques, called rough set theory, is applied to derive rules for the process variables that contribute to the surface roughness. The proposed rule-composing algorithm and rule-validation procedure have been tested with the historical data the company has collected over the years. The results indicate a higher accuracy over the statistical approaches in terms of predicting acceptance level of surface roughness.     

基于粗糙集与K-均值聚类的故障知识挖掘

针对连续数据故障诊断知识挖掘,提出了一种将粗糙集理论与K-均值聚类算法相结合的故障诊断知识挖掘方法。该方法在提取设备状态参数数据的基础上,应用K-均值聚类算法将各状态参数下的连续数据离散化为有限类别,再应用粗糙集对所获得的离散数据表进行约简,获得由状态数据出发的故障诊断知识表格。应用于柴油机故障诊断数据知识挖掘,可以快速准确地获得故障诊断知识,方法简单易用。     

A rough set approach to analyze factors affecting landslide incidence

Landslide incidence can be affected by a variety of environmental factors. Past studies have focused on the identification of these environmental factors, but most are based on statistical analysis. In this paper, spatial information techniques were applied to a case study of landslide occurrence in China by combining remote sensing and geographical information systems with an innovative data mining approach (rough set theory) and statistical analyses. Core and reducts of data attributes were obtained by data mining based on rough set theory. Rules for the impact factors, which can contribute to landslide occurrence, were generated from the landslide knowledge database. It was found that all 11 rules can be classified as both exact and approximate rules. In terms of importance, three main rules were then extracted as the key decision-making rules for landslide predictions. Meanwhile, the relationship between landslide occurrence and environmental factors was statistically analyzed to validate the accuracy of rules extracted by the rough set-based method. It was shown that the rough set-based approach is of use in analyzing environmental factors affecting landslide occurrence, and thus facilitates the decision-making process for landslide prediction.     

基于粗糙集理论的规则修正方法

基于粗糙集理论分别给出了确定性决策规则和可能性决策规则的获取与修正的理论和方法，给出一种利用粗糙集理论解决在增加样本数量情况下的动态规则获取方法，滚动轴承的故障诊断实例证明了该方法的有效性。     

基于粗糙集和神经网络的柱塞泵故障诊断

针对轴向柱塞泵故障机理的复杂性和故障信息的不确定性,提出了基于粗糙集与神经网络相结合的故障诊断方法,并详细阐述了基于粗糙集与神经网络的轴向柱塞泵故障诊断系统的设计步骤和实现技术。实验结果表明,该方法不仅能优化神经网络的拓扑结构,同时能有效提高轴向柱塞泵故障诊断的精度和效率。     

A classification model based on SVM and rough set theory

This paper presents a novel classification approach based on rough set theory and supporter vector machine. Sometimes, there are many attributes for classification samples and it is difficult to carry out classification. In this paper, the attributes of data set are reduction by rough set theory firstly, and then the classification is carried out using support vector machine. Finally, the classification results are obtained through the proposed model. Moreover, the proposed classification model has higher prediction accuracy by comparing with the traditional algorithm Naive Bayes algorithm and reduces the cost of calculation.     

一种基于粗集理论的车地无线通信设备故障诊断方法*

针对车地无线通信设备故障诊断信息不一致的情况下进行故障的推理和诊断,提出了一种基于粗集理论的车地无线通信装备故障诊断方法。该方法从诊断决策支持系统定义出发,将车地无线通信设备故障诊断问题用一个不同简化层次的故障决策网络表示,由网络节点根据定义出的规则置信度和覆盖度可推导出对应有效的故障诊断规则集合。通过故障诊断规则匹配,便可方便得出车地通信单元故障诊断结果,实例分析表明该方法具有较好诊断有效性。     

A rough set-based fault ranking prototype system for fault diagnosis

Fault diagnosis is a complex and difficult problem that concerns effective decision-making. Carrying out timely system diagnosis whenever a fault symptom is detected would help to reduce system down time and improve the overall productivity. Due to the knowledge and experience intensive nature of fault diagnosis, the diagnostic result very much depends on the preference of the decision makers on the hidden relations between possible faults and the presented symptom. In other words, fault diagnosis is to rank the possible faults accordingly to give the engineer a practical priority to carry out the maintenance work in an efficient and orderly manner. This paper presents a rough set-based prototype system that aims at ranking the possible faults for fault diagnosis. The novel approach engages rough theory as a knowledge extraction tool to work on the past diagnostic records, which is registered in a pair-wise comparison table. It attempts to extract a set of minimal diagnostic rules encoding the preference pattern of decision-making by domain experts. By means of the knowledge acquired, the ordering of possible faults for failure symptom can then be determined. The prototype system also incorporates a self-learning ability to accumulate the diagnostic knowledge. A case study is used to illustrate the functionality of the developed prototype. Result shows that the ranking outcome of the possible faults is reasonable and sensible.