Jun Zhao

Witold Pedrycz

Sabrina Senatore

Data Mining and Knowledge Discovery in Industrial Engineering

Due to its importance in process improvement, the issue of determining exactly when faults occur has attracted considerable attention in recent years. Most related studies have focused on the use of the maximum likelihood estimator (MLE) method to determine the fault in univariate processes, in which the underlying process distribution should be known in advance. In addition, most studies have been devoted to identifying the faults of process mean shifts. Different from most of the current research, the present study proposes an effective approach to identify the faults of variance shifts in a multivariate process. The proposed mechanism comprises the analysis of variance (ANOVA) approach, a neural network (NN) classifier, and an identification strategy. To demonstrate the effectiveness of our proposed approach, a series of simulated experiments is conducted, and the best results from our proposed approach are addressed.

Yuehjen E. Shao

Chia-Ding Hou

Fault Identification in Industrial Processes Using an Integrated Approach of Neural Network and Analysis of Variance

Fault diagnosis of power systems is an important task in power system operation. In this paper, fuzzy reasoning spiking neural P systems (FRSN P systems) are implemented for fault diagnosis of power systems for the first time. As a graphical modeling tool, FRSN P systems are able to represent fuzzy knowledge and perform fuzzy reasoning well. When the cause-effect relationship between candidate faulted section and protective devices is represented by the FRSN P systems, the diagnostic conclusion can be drawn by means of a simple parallel matrix based reasoning algorithm. Three different power systems are used to demonstrate the feasibility and effectiveness of the proposed fault diagnosis approach. The simulations show that the developed FRSN P systems based diagnostic model has notable characteristics of easiness in implementation, rapidity in parallel reasoning, and capability in handling uncertainties. In addition, it is independent of the scale of power system and can be used as a reliable tool for fault diagnosis of power systems.

Guojiang Xiong

Dongyuan Shi

Lin Zhu

Xianzhong Duan

A New Approach to Fault Diagnosis of Power Systems Using Fuzzy Reasoning Spiking Neural P Systems

Sharing nearest neighbor (SNN) is a novel metric measure of similarity, and it can conquer two hardships: the low similarities between samples and the different densities of classes. At present, there are two popular SNN similarity based clustering methods: JP clustering and SNN density based clustering. Their clustering results highly rely on the weighting value of the single edge, and thus they are very vulnerable. Motivated by the idea of smooth splicing in computing geometry, the authors design a novel SNN similarity based clustering algorithm within the structure of graph theory. Since it inherits complementary intensity-smoothness principle, its generalizing ability surpasses those of the previously mentioned two methods. The experiments on text datasets show its effectiveness.

JingDong Tan

RuJing Wang

Smooth Splicing: A Robust SNN-Based Method for Clustering High-Dimensional Data

In practical engineerings, structures are often excited by different kinds of loads at the same time. How to effectively analyze and simulate this kind of dynamic environment of structure, named combined dynamic environment, is one of the key issues. In this paper, a novel prediction method of combined dynamic environment is proposed from the perspective of data analysis. First, the existence of dynamic similarity between vibration responses of the same structure under different boundary conditions is theoretically proven. It is further proven that this similarity can be established by a multiple-input multiple-output regression model. Second, two machine learning algorithms, multiple-dimensional support vector machine and extreme learning machine, are introduced to establish this model. To test the effectiveness of this method, shock and stochastic white noise excitations are acted on a cylindrical shell with two clamps to simulate different dynamic environments. The prediction errors on various measuring points are all less than &#xb1;3&#x2009;dB, which shows that the proposed method can predict the structural vibration response under one boundary condition by means of the response under another condition in terms of precision and numerical stability.

Wentao Mao

Guirong Yan

Longlei Dong

A Novel Machine Learning Based Method of Combined Dynamic Environment Prediction

A model named KaM&#x5f;CRK is proposed, which can supply the clustered and ranked knowledge to the users on different contexts. By comparing the attributes of contexts and JANs, our findings indicate that our model can accumulate the JANs, whose attributes are similar with the user&#x2019;s contexts, together. By applying the KaM&#x5f;CLU algorithm and Centre rank strategy into the KaM&#x5f;CRK model, the model boosts a significant promotion on the accuracy of provision of user's knowledge. By analyzing the users' behaviors, the dynamic coefficient BehaviorF is first presented in KaM&#x5f;CLU. Compared to traditional approaches of K&#x5f;means and DBSCAN, the KaM&#x5f;CLU algorithm does not need to initialize the number of clusters. Additionally, its synthetic results are more accurate, reasonable, and fit than other approaches for users. It is known from our evaluation through real data that our strategy performs better on time efficiency and user's satisfaction, which will save by 30&#x25; and promote by 5&#x25;, respectively.

Changhong Hu

Shufen Liu

Ramana Reddy

Sumitra Reddy

Mingyang Liu

KaM&#x5f;CRK: Clustering and Ranking Knowledge for Reasonable Results Based on Behaviors and Contexts

Many data mining methods have been proposed to improve the precision and accuracy of job cycle time forecasts for wafer fabrication factories. This study presents a fuzzy data mining approach based on an innovative fuzzy backpropagation network (FBPN) that determines the lower and upper bounds of the job cycle time. Forecasting accuracy is also significantly improved by a combination of principal component analysis (PCA), fuzzy c-means (FCM), and FBPN. An applied case that uses data collected from a wafer fabrication factory illustrates this fuzzy data mining approach. For this applied case, the proposed methodology performs better than six existing data mining approaches.

Toly Chen

Richard Romanowski

Precise and Accurate Job Cycle Time Forecasting in a Wafer Fabrication Factory with a Fuzzy Data Mining Approach

Support vector machine (SVM) has been applied very successfully in a variety of classification systems. We attempt to solve the primal programming problems of SVM by converting them into smooth unconstrained minimization problems. In this paper, a new twice continuously differentiable piecewise-smooth function is proposed to approximate the plus function, and it issues a piecewise-smooth support vector machine (PWSSVM). The novel method can efficiently handle large-scale and high dimensional problems. The theoretical analysis demonstrates its advantages in efficiency and precision over other smooth functions. PWSSVM is solved using the fast Newton-Armijo algorithm. Experimental results are given to show the training speed and classification performance of our approach.

Qing Wu

Wenqing Wang

Piecewise-Smooth Support Vector Machine for Classification

Reliable feature extraction from 3D point cloud data is an important problem in many application domains, such as reverse engineering, object recognition, industrial inspection, and autonomous navigation. In this paper, a novel method is proposed for extracting the geometric features from 3D point cloud data based on discrete curves. We extract the discrete curves from 3D point cloud data and research the behaviors of chord lengths, angle variations, and principal curvatures at the geometric features in the discrete curves. Then, the corresponding similarity indicators are defined. Based on the similarity indicators, the geometric features can be extracted from the discrete curves, which are also the geometric features of 3D point cloud data. The threshold values of the similarity indicators are taken from <svg style="vertical-align:-2.22495pt;width:34.887501px;" id="M1" height="14.85" version="1.1" viewBox="0 0 34.887501 14.85" width="34.887501" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
			<g transform="matrix(.017,-0,0,-.017,.062,12.012)"><path id="x5B" d="M290 -163h-170v866h170v-28q-79 -7 -94 -19.5t-15 -72.5v-627q0 -59 14.5 -71.5t94.5 -19.5v-28z" /></g><g transform="matrix(.017,-0,0,-.017,5.927,12.012)"><path id="x30" d="M241 635q53 0 94 -28.5t63.5 -76t33.5 -102.5t11 -116q0 -58 -11 -112.5t-34 -103.5t-63.5 -78.5t-94.5 -29.5t-95 28t-64.5 75t-34.5 102.5t-11 118.5q0 58 11.5 112.5t34.5 103t64.5 78t95.5 29.5zM238 602q-32 0 -55.5 -25t-35.5 -68t-17.5 -91t-5.5 -105&#xA;q0 -76 10 -138.5t37 -107.5t69 -45q32 0 55.5 25t35.5 68.5t17.5 91.5t5.5 105t-5.5 105.5t-18 92t-36 68t-56.5 24.5z" /></g><g transform="matrix(.017,-0,0,-.017,14.086,12.012)"><path id="x2C" d="M95 130q31 0 61 -30t30 -78q0 -53 -38 -87.5t-93 -51.5l-11 29q77 31 77 85q0 26 -17.5 43t-44.5 24q-4 0 -8.5 6.5t-4.5 17.5q0 18 15 30t34 12z" /></g><g transform="matrix(.017,-0,0,-.017,20.784,12.012)"><path id="x31" d="M384 0h-275v27q67 5 81.5 18.5t14.5 68.5v385q0 38 -7.5 47.5t-40.5 10.5l-48 2v24q85 15 178 52v-521q0 -55 14.5 -68.5t82.5 -18.5v-27z" /></g><g transform="matrix(.017,-0,0,-.017,28.943,12.012)"><path id="x5D" d="M226 -163h-170v27q79 7 94 20t15 73v627q0 59 -15 72t-94 20v27h170v-866z" /></g>

		
	
</svg>, which characterize the relative relationship and make the threshold setting easier and more reasonable. The experimental results demonstrate that the proposed method is efficient and reliable.

Yi An

Zhuohan Li

Cheng Shao

Feature Extraction from 3D Point Cloud Data Based on Discrete Curves

Integrality and validity of industrial data are the fundamental factors in the domain of data-driven modeling. Aiming at the data missing problem of gas flow in steel industry, an improved Generalized-Trend-Diffusion (iGTD) algorithm is proposed in this study, where in particular it considers the sort of problem with data properties of consecutively missing and small samples. And, the imputation accuracy can be greatly increased by the proposed Gaussian membership-based GTD which expands the useful knowledge of data samples. In addition, the imputation order is further discussed to enhance the sequential forecasting accuracy of gas flow. To verify the effectiveness of the proposed method, a series of experiments that consists of three categories of data features in the gas system is presented, and the results indicate that this method is comprehensively better for the imputation of the periodical-like data and the time-series-like data.

Ying Liu

Zheng Lv

Wei Wang

An Improved Generalized-Trend-Diffusion-Based Data Imputation for Steel Industry

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Mathematical Problems in Engineering is a broad-based journal publishing&nbsp;results of rigorous engineering research across all disciplines, carried out using mathematical tools.

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Chief Editor, Professor Guangming Xie, is currently a full professor of dynamics and control with the College of Engineering, Peking University. His research interests include complex system dynamics and control and intelligent and biomimetic robots.

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Mathematical Problems in Engineering

Data Mining and Knowledge Discovery in Industrial Engineering

Data Mining and Knowledge Discovery in Industrial Engineering

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