基于粗糙集理论的系统建模与故障诊断

应用粗糙集理论,可以从原始的数据中提取有用的知识或规则.依据这一思想,本文建立了一个实际非线性系统的粗糙集模型.在建模过程中,首先用系统输入输出的采样数据构成原始信息表,然后离散化,再利用粗糙集算法得到系统粗糙集模型的不完备规则集,通过实验和线性插补法实现规则集完备化,最后完成模型的设计和校验.校验结果表明所建的粗糙集模型是有效的,并利用该模型实现了系统的一种故障诊断.     

A rough set approach to design concept analysis in a design chain

The inherent dynamic relationships among design tasks performed concurrently at different organizations characterize the complexities of a design chain where designers with diverse expertise need to collaborate across organizational boundaries. To ensure timely completion of inter-related design tasks, metrics to facilitate the early evaluation of design concepts are crucial. The ability to evaluate and select suitable design concepts at an early stage will ensure better solutions and greater savings in time and effort further downstream. This paper proposes a new approach based on the rough set theory to design concept analysis. The approach aims at early detection of design inadequacy. A so-called information system is constructed using the information gleaned from design concepts and design capabilities, and analyzed using the rough set theory to derive a set of design rules for design concept analysis. The approach embodies a technique for handling attributes with unavailable information, which is a frequent occurrence in design. This paper presents details of the proposed approach, the novel technique, and a case study.     

粗糙集合理论在旋转机械故障诊断技术中的应用

粗糙集合理论基于严格的集合分析方法,通过对数据集合进行等价关系、近似空间、分类等运算,发现隐含在数据中的规律与性质,从而完成知识发现。在人工智能机器故障诊断系统中,如何获得故障诊断等知识成为关键技术。论文提出将粗糙集合理论应用于加工中心故障诊断技术,使得故障诊断技术中的知识获取瓶颈问题得以有效解决。     

应用聚类和粗糙集的FPGA底层数据挖掘方法

提出了一种从XDL文件中提取现场可编程门阵列(FPGA)底层逻辑和布线资源的方法。预处理阶段通过正则表达式将原有底层逻辑文件转换为待分析的有层次属性的关系型数据库。数据挖掘阶段则根据各个层次数据内部的特性不同,采用不同的算法进行聚类来得到初步知识。通过粗糙集分析初步知识间的关系和约简属性,得出初步知识间的联系同时进一步提取出决策规则和产生式规则的知识。最后,通过规则验证器和泛化器对提取出的规则进行验证和泛化。实验结果表明,对于大型的FPGA器件,wire的逻辑最高压缩比可以达到2.88×10-4。该方法相对于底层器件有较好的通用性和交互性,适用于对不同器件族FPGA底层信息的知识提取,对深入研究FPGA的拓扑架构,提高对FPGA进行动态重配置的可控性和实现更灵活的重配置很有意义。     

基于小波包与粗集的往复压缩机故障诊断方法

提出了基于小波包与粗集的往复压缩机故障诊断的新方法。该方法使故障特征提取及规则的提取都由计算机自动完成，经诊断实例表明，使用该方法可对往复压缩机进行有效的故障诊断。     

基于粗集理论故障特征值提取模糊识别变速箱故障方法的研究

运用粗集理论简化所提取变速箱部分特征值，提高了变速箱故障诊断的效率。将模糊数学理论应用到变速箱故障识别中，模糊故障识别是利用的模糊集合论中的隶属函数及模糊关系矩阵的概念，解决故障与征兆间关系的模糊不确定性进行故障种类的识别。构造隶属函数及分段函数区间的确定是诊断成败的关键。     

粗糙集理论在旋转机械实时故障诊断中的应用

根据旋转机械实时故障诊断的实际需求,引入粗糙集理论中的决策模型,作为典型故障诊断规则的发现工具。并针对故障信号的非平稳性和诊断分析的实时性要求,采用小波包分析(WPA)作为现场数据的频域段特征的提取工具。并首次将小波包分析(WPA)与粗糙集理论的决策模型相结合,提出了适应于现代机械设备在线诊断的故障分析模型WRS。并通过实例,验证了全过程。     

基于Rough集数据挖掘模型的钻头磨损状态预报系统

利用Rough集数据挖掘模型分析主电机电流、钻头直径、切削参数等因素与钻头磨损的规律 ,建立通过监测主电机电流信号对钻头磨损状态进行预报的监测系统     

A hybrid genetic algorithm approach to mixed-model assembly line balancing

Assembly line balancing has been a focus of interest to academics in operation management for the last four decades. Mass production has saved huge costs for manufacturers in various industries for some time. With the growing trend of greater product variability and shorter life cycles, traditional mass production is being replaced in assembly lines. The current market is intensely competitive and consumer-centric. Mixed-model assembly lines are increasing in many industrial environments. This study deals with mixed-model assembly line balancing for n models, and uses a classical genetic algorithm approach to minimize the number of workstations. We also incorporated a hybrid genetic algorithm approach that used the solution from the modified ranked positional method for the initial solution to reduce the search space within the global space, thereby reducing search time. Several examples illustrate the approach. The software used for programming is C++ language .     

基于随机集理论的并发故障诊断信息融合方法

为了诊断并发故障,提出一种基于随机集理论的信息融合方法.首先构造包含并发故障的论域,并在此论域的超幂集上定义扩展型随机集.基于该随机集和广义集值映射给出证据组合规则的随机集模型,用其构造可以同时适用于单发和并发故障诊断的新型组合规则.此外,根据传感器提供的故障信息构造故障样板模式与待检模式的模糊隶属度函数,利用模糊集的随机集表示以及随机集似然测度,获得两种模式匹配的程度作为待融合的诊断证据.最后通过在电机柔性转子平台上的试验,证明了所提方法可有效地减少单一传感器信息诊断的不确定性,显著提高转子系统故障诊断的精度.     

Data mining diagnosis system based on rough set theory for boilers in thermal power plants

Large amounts of data in the SCADA systems’ databases of thermal power plants have been used for monitoring, control and over-limit alarm, but not for fault diagnosis. Additional tests are often required from the technology support center of manufacturing companies to diagnose faults for large-scale equipment, although these tests are often expensive and involve some risks to equipment. Aimed at difficulties in fault diagnosis for boilers in thermal power plants, a hybrid-intelligence data-mining system based only on acquired data in SCADA systems is structured to extract hidden diagnosis information directly from the SCADA systems’ databases in thermal power plants. This makes it possible to eliminate additional tests for fault diagnosis. In the system, a focusing quantization algorithm is proposed to discretize all variables in the preparation set to improve resolution near the change between normal value to abnormal value. A reduction algorithm based on rough set theory is designed to find minimum reducts from all discrete variables in the preparation set to represent diagnosis rules succinctly. The diagnosis rules mining from SCADA systems’ database are expressed directly by variables in the database, making it easy for engineers to understand and use in industry applications. A boiler fault diagnosis system is designed and realized by the proposed approach, its running results in a thermal power plant of Guangdong Province show that the system can satisfy fault diagnosis requirement of large-scale boilers and its accuracy rangers from 91% to 98% in different months.     

A data mining approach for fault diagnosis: An application of anomaly detection algorithm

Rolling-element bearing failures are the most frequent problems in rotating machinery, which can be catastrophic and cause major downtime. Hence, providing advance failure warning and precise fault detection in such components are pivotal and cost-effective. The vast majority of past research has focused on signal processing and spectral analysis for fault diagnostics in rotating components. In this study, a data mining approach using a machine learning technique called anomaly detection (AD) is presented. This method employs classification techniques to discriminate between defect examples. Two features, kurtosis and Non-Gaussianity Score (NGS), are extracted to develop anomaly detection algorithms. The performance of the developed algorithms was examined through real data from a test to failure bearing. Finally, the application of anomaly detection is compared with one of the popular methods called Support Vector Machine (SVM) to investigate the sensitivity and accuracy of this approach and its ability to detect the anomalies in early stages.     

基于矢谱和粗糙集理论的旋转机械故障诊断

矢谱融合了转子同源双通道的信息,能准确反映转子运动状态．粗糙集理论是一种对决策表进行简化,去除冗余属性的数据分析和处理方法．提出了基于矢谱和粗糙集理论的旋转机械故障诊断方法．计算了旋转机械振动4种典型故障的矢谱征兆,使用粗糙集理论对其进行约简,根据约简的结果生成矢谱诊断规则,并利用得到的规则对故障测试样本进行了诊断．结果表明：相对于单通道数据,基于矢谱和粗糙集理论的故障诊断不仅简化了诊断规则,而且明显提高了故障诊断的准确率．     

基于粗糙集-RBF神经网络的水电机组故障诊断

由于水电机组监测数据量过大,基于神经网络的故障诊断存在网络结构复杂,训练时间长的问题,本文将粗糙集理论引入到水电机组故障诊断中,提出了基于粗糙集理论与RBF神经网络相结合的水电机组故障诊断方法。利用粗糙集理论在处理不确定信息方向的优点,在保持分类能力不变的前提下,去掉机组的冗余信息,保留必要的要素,并结合RBF神经网络对预处理后的信息进行诊断,使神经网络的输入神经元数目明显减少,其结构也得以简化,可以有效地提高故障诊断准确度。通过对实测机组振动数据进行诊断,证明了该诊断方法的有效性。     

Rough set based rule learning and fuzzy classification of wavelet features for fault diagnosis of monoblock centrifugal pump

The fault diagnosis problem is conceived as a classification problem. In the present study, vibration signals are used for fault diagnosis of centrifugal pumps using wavelet analysis. Rough set theory is applied to generate the rules from the vibration signals. Based on the strength of the rules the faults are identified. The different faults considered for this study are: pump at good condition, cavitation, pump with faulty impeller, pump with faulty bearing and pump with both faulty bearing and impeller. However, the classification accuracy is based on the strength and number of rules generated using rough set theory. Wavelet features are computed using Discrete Wavelet Transform (DWT) from the vibration signals and rules are generated using rough sets and classified using fuzzy logic. The results are presented in the form of confusion matrix which shows the classification capability of wavelet features with rough set and fuzzy logic for fault diagnosis of monoblock centrifugal pump.     

Module level fault diagnosis for analog circuits based on system identification and genetic algorithm

This paper proposes a new module level fault diagnosis method for analog circuits. Firstly, the transfer function is constructed according to the relationship between output and input of the circuit under test (CUT). Every system parameter of the transfer function is expressed by several component parameters. These components are divided into several modules. Then, the way of objective function optimization based on genetic algorithm (GA) is adopted to solve nonlinear equations, which are obtained by multi-frequency testing. Finally, the module level faults are detected by comparing the estimated system parameters to their normal values. The results show that the proposed method is effective to identify system parameters and locate module level faults.     

A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm

A novel intelligent diagnosis model based on wavelet support vector machine (WSVM) and immune genetic algorithm (IGA) for gearbox fault diagnosis is proposed. Wavelet support vector machine is a powerful novel tool for solving the diagnosis problem with small sampling, nonlinearity and high dimension. Immune genetic algorithm is developed in this study to determine the optimal parameters for WSVM with the highest accuracy and generalization ability. Moreover, the feature vectors for fault diagnosis are obtained from vibration signal that preprocessed by empirical mode decomposition (EMD). The experimental results indicate that this proposed approach is an effective method for gearbox fault diagnosis, which has more strong generalization ability and can achieve higher diagnostic accuracy than that of the artificial neural network and the SVM which has randomly extracted parameters.     

Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker

Based on empirical mode decomposition (EMD) method and support vector machine (SVM), a new method for the fault diagnosis of high voltage circuit breaker (CB) is proposed. The feature extraction method based on improved EMD energy entropy is detailedly analyzed and SVM is employed as a classifier. Radial basis function (RBF) is adopted as the kernel function of SVM and its kernel parameter γ and penalty parameter C must be carefully predetermined in establishing an efficient SVM model. Therefore, the purpose of this study is to develop a genetic algorithm-based SVM (GA-SVM) model that can determine the optimal parameters of SVM with the highest accuracy and generalization ability. The classification accuracy of this GA-SVM approach is tried by real dataset and compared with the SVM, which has randomly selected kernel function parameters. The experimental results indicate that the classification accuracy of this GA-SVM approach is more superior than that of the artificial neural network and the SVM which has constant and manually extracted parameters.     

基于粗集理论的知识挖掘及其在工程中的应用研究

本文探索了知识挖掘和粗集理论的基本原理与方法，阐述了基于粗集理论知识挖掘的实现方法，介绍了该方法在工程中的应用。     

粗糙集理论和神经网络在柴油机故障诊断中的应用

将粗糙集理论与人工神经网络相结合,主要研究柴油机故障特征的提取与优化等问题,目的在于优化、缩减神经网络的输入向量,缩短网络的训练和执行时间,最终实现提高诊断的准确率与效率。