A possibilistic clustering approach to novel fault detection and isolation

In this paper, a new approach for fault detection and isolation that is based on the possibilistic clustering algorithm is proposed. Fault detection and isolation (FDI) is shown here to be a pattern classification problem, which can be solved using clustering and classification techniques. A possibilistic clustering based approach is proposed here to address some of the shortcomings of the fuzzy c-means (FCM) algorithm. The probabilistic constraint imposed on the membership value in the FCM algorithm is relaxed in the possibilistic clustering algorithm. Because of this relaxation, the possibilistic approach is shown in this paper to give more consistent results in the context of the FDI tasks. The possibilistic clustering approach has also been used to detect novel fault scenarios, for which the data was not available while training. Fault signatures that change as a function of the fault intensities are represented as fault lines, which have been shown to be useful to classify faults that can manifest with different intensities. The proposed approach has been validated here through simulations involving a benchmark quadruple tank process and also through experimental case studies on the same setup. For large scale systems, it is proposed to use the possibilistic clustering based approach in the lower dimensional approximations generated by algorithms such as PCA. Towards this end, finally, we also demonstrate the key merits of the algorithm for plant wide monitoring study using a simulation of the benchmark Tennessee Eastman problem.     

一种智能型的PCB产业需求预测方法

目前需求预测在整个印制电路板产业的生产活动控制中正扮演着越来越重要的角色。分析了影响印制电路板需求的因素和现有的预测方法,提出了一种适用于PCB产业需求预测的有效方法——遗传/BP—神经网络。实验表明该方法能够进一步改善印制电路板预测的准确度和减少生产成本的消耗。     

Study of elastic wave propagation induced by piezoelectric actuators for crack identification

The current paper provides a theoretical study of elastic wave propagation in a cracked elastic medium induced by an embedded piezoelectric actuator. The formulation of the problem is based on a newly developed actuator model and the solution of coupled crack and actuator problems. The interaction between an actuator and a crack is studied to develop basic understanding of the effect of the crack upon the wave field generated. The resulting wave field is used to simulate 'sensor signals'. Scattered wave from the crack is then regenerated by solving a reverse elastodynamic problem and used in an imaging technique to identify the crack.     

Prediction Model for BTA Deep-Hole Machining using Fuzzy Clustering Approach: Experimental Study

In this article, a subtractive clustering-based fuzzy identification method and a Sugeno-type fuzzy inference system are used for modeling in metal cutting. This approach is considered with its application on the experimental study of Boring and Trepanning Association (BTA) deep-hole drilling. The model for the surface roughness is identified by using the cutting speed and feed as input data and roughness as the output data. Using subtractive clustering in both input and output spaces performs the model-building process. Minimum error model is obtained through enumerative search of clustering parameters. The fuzzy model obtained is capable of predicting the surface roughness for a given set of inputs (speed and feed). Therefore, the operator can predict the quality of the surface for a given set of working parameters and will then be able to set the machining parameters to achieve a certain surface quality. The fuzzy model is verified experimentally by further experimentation using different sets of inputs. The tool life is also investigated using the same approach. The fuzzy inference system obtained is capable of predicting the tool life for a given set of cutting parameters. Therefore, the operator will be able to predict how many minutes the cutting tool is going to last and will set the time for the next tool change.     

介质波导阵列的横向传播特性分析

根据已提出的有关二维周期介质严格分析理论，针对介质波导阵列进行了大量的数值计算。对介质波导阵列中波的横向传播作了详细分析。首先，在给定纵向波矢量(k)的情况下，分析了在不同工作频率时的横向波矢量间布里渊区色散特性，对空间谐波的相互作用情况也进行了研究。其次，在给定工作频率的情况下，分析了不同纵向波矢量和不同调制系数时的横向波矢量间布里渊区色散特性。由于空间谐波的相互作用随纵向波数或调制系数的增大而增强，导致阻带能明显地表现出来，从而使介质波导阵列的横向传播特性能较为全面地得到分析。     

一种T-S型动态模糊推理模型的建模算法研究

以一种进化聚类算法（ECM）为基础,提出了一种新的T-S型动态模糊推理模型的建模算法.以往许多神经模糊模型都不适用于自适应在线学习,而文章模型能够实时地调整模糊规则库及规则参数,具有较强的在线学习能力.仿真结果表明,该方法是有效的.     

Wavefront reduction using graphs,neural networks and genetic algorithm

This paper presents an algorithm for nodal numbering in order to obtain a small wavefront. Element clique graphs are employed as the mathematical models of finite element meshes. A priority function containing five vectors is used, which can be viewed as a generalization of Sloan's function. These vectors represent different connectivity properties of the graph models. Unlike Sloan's algorithm, which uses two fixed coefficients, here, five coefficients are employed, based on an evaluation by artificial neural networks. The networks weights are obtained using a simple genetic algorithm. Examples are included to illustrate the performance of the present hybrid method. Copyright © 2004 John Wiley & Sons, Ltd.     

市区微蜂窝传播路径损耗模式的比较检验

讨论了几种微蜂窝路径损耗模式,并用我国某城市测量的微蜂窝传播数据对COST231WI,修正COST231WI和Xia模式进行了检验,结果表明修正COST231模式的误差最小,Xia模式次之,COST231WI模式最差。从而得到修正COST231模式比较符合我国实际的微蜂窝传播预测模式。     

有序聚类法在锦凌水库风化岩体定量分带中的应用

应用有序聚类法对辽宁省锦凌水库坝基的风化岩体进行定量分带,所得结果与地质观察方法、规范法等所得出的结果进行了分析比较.采用岩体风化差异系数法进行综合分析,最终推荐按照有序聚类方法进行风化岩体分带.     

基于聚类算法实现信号盲分类

本文解决了信号处理、工业控制等领域存在的非平稳信号盲分类问题。在聚类中广泛应用的K-Means算法及其它基于中心的聚类算法有两个共同的缺陷-需要预先确定类数目且随机初始化中心引起性能不稳定。本文提出的算法较好地解决了这两个问题，提高了算法稳定性，实现了非平稳信号盲分类。提取非平稳信号的小波系数作为聚类的样本空间，分析聚类结果的统计偏差以估计类的数目，采用调和均值准则进行分类。最后给出的仿真结果表明本文提出的方法较传统的K-Means算法明显降低分类错误率。