Chun-Chin Hsu

An Adaptive Forecast-Based Chart for Non-Gaussian Processes Monitoring: With Application to Equipment Malfunctions Detection in a Thermal Power Plant

In order to ensure power quality and keep supplying power in a thermal power plant, early detection of equipment malfunctions is a critical issue. This study attempts to develop an adaptive forecast-based chart so as to enhance the fault detectability in a thermal power plant. In the proposed monitoring statistic, the exponentially weighted moving average is adopted to preserve the information of past observations. Simultaneously, independent component analysis (ICA) is used to extract non-Gaussian information. The advantages of the proposed statistic include the fact that it is capable of monitoring non-Gaussian processes, the detection of small process shifts is improved, and the traditional ICA chart is a special case of the proposed one. The efficiency of the proposed method is verified by a simulated process and a real case of thermal power plant of Taiwan Power Company. Results demonstrated that the proposed method outperforms conventional monitoring methods, especially for detecting small process changes.

An association-based case reduction technique for case-based reasoning

Case-based reasoning (CBR) is a type of problem solving technique which uses previous cases to solve new, unseen and different problems. Although a larger number of cases in the memory can improve the coverage of the problem space, the retrieval efficiency will be downgraded if the size of the case-base grows to an unacceptable level. In CBR systems, the tradeoff between the number of cases stored in the case-base and the retrieval efficiency is a critical issue. This paper addresses the problem of case-base maintenance by developing a new technique, the association-based case reduction technique (ACRT), to reduce the size of the case-base in order to enhance the efficiency while maintaining or even improving the accuracy of the CBR. The experiments on 12 UCI datasets and an actual case from Taiwans hospital have shown superior generalization accuracy for CBR with ACRT (CBR-ACRT) as well as a greater solving efficiency.

Intelligent ICA-SVM fault detector for non-Gaussian multivariate process monitoring

Recently, the independent component analysis (ICA) has been widely used for multivariate non-Gaussian process monitoring. For principal component analysis (PCA) based monitoring method, the control limit can be determined by a specific distribution (F distribution) due to the PCA extracted components are assumed to follow multivariate Gaussian distribution. However, the control limit for ICA based monitoring statistic is determined by using kernel density estimation (KDE). It is well known that the KDE is sensitive to the smoothing parameter, and it does not perform well with autocorrelated data. In most cases, the calculated ICA based monitoring statistic is usually autocorrelated. Thus, this study aims to integrate ICA and support vector machine (SVM) in order to develop an intelligent fault detector for non-Gaussian multivariate process monitoring. Simulation study indicates that the proposed method can effectively detect faults when compare to methods of original SVM and PCA based SVM in terms of detection rate.

Integrating independent component analysis and support vector machine for multivariate process monitoring

This study aims to develop an intelligent algorithm by integrating the independent component analysis (ICA) and support vector machine (SVM) for monitoring multivariate processes. For developing a successful SVM-based fault detector, the first step is feature extraction. In real industrial processes, process variables are rarely Gaussian distributed. Thus, this study proposes the application of ICA to extract the hidden information of a non-Gaussian process before conducting SVM. The proposed fault detector will be implemented via two simulated processes and a case study of the Tennessee Eastman process. Results demonstrate that the proposed method possesses superior fault detection when compared to conventional monitoring methods, including PCA, ICA, modified ICA, ICA-PCA and PCA-SVM.

C-Kano model: a novel approach for discovering attractive quality elements

The theory of attractive quality (the Kano model) can offer a better understanding of how customers evaluate products, and helps practitioners to focus on the most important quality attributes to improve. However, in practice, users of the Kano model usually cannot find an attractive or one-dimensional quality element due to an improperly-designed questionnaire, poorly-defined quality attributes or lifecycle of quality attributes. Kano also indicated that attractive quality creation to bring a must-be product back to an attractive product is a tough task. To solve these issues, this study proposed a novel Creativity-based Kano model (C-Kano model) which integrates the creativity techniques, TRIZ and SCAMPER, into the traditional Kano model. The proposed C-Kano model not only discovers customer needs, but also creates attractive quality elements. In this study, an actual quality survey of Massively Multiplayer Online Role Playing Game (MMORPG) was used to evaluate the effectiveness of the C-Kano model. Compared with the traditional Kano model, the experimental results show that our proposed model is superior in discovering attractive quality elements.

A process monitoring scheme based on independent component analysis and adjusted outliers

Recently, the independent component analysis (ICA) has been widely used for non-Gaussian multivariate process monitoring. An elliptical type measure is traditionally used for ICA-based process monitoring. However, it will not work appropriately since the extracted ICA components exhibit skewed distribution. Thus, this study aims to develop a novel process monitoring scheme for ICA. The basic idea of the proposed method is to first screen out outliers in order to describe well majority for training dataset. Hereafter, a rectangular type measure is applied to monitor the process. The efficiency of proposed monitoring scheme will be implemented via a five variables simulation example and a case study of Tennessee Eastman process. Results indicate that the proposed method cannot only deal with the contaminated training dataset but also shows superior fault detection ability when compared with alternative methods.

An innovative approach for RFID product functions development

Today, new services creation is very crucial for RFID (radio frequency identification) products manufacturers. A newly successful RFID application can enhance their change in organization and to manage growth in an increasingly competitive environment. But, there is a high failure rate in new products development processes. Thus, RFID manufacturers need an effective tool to assist them to create novel RFID product functions. This study proposes a newly systematic approach called QT-Kano model which integrates three management tools, quality function deployment (QFD), the theory of inventive problem solving (TRIZ), and a refined Kanos model, to create new product functions of RFID products. In QT-Kano model, QFD has firstly been used to transform customer demands into engineering quality characteristics. Secondly, based on the contradiction relationship between those engineering quality characteristics, the novel designed functions are created by TRIZ. Finally, to reduce the high failure rate in new products development processes, a refined Kanos model are applied to offer a better understanding from customers viewpoint and to assist service designers focusing on the most important attributes that need to be improved. A real case of RFID product function development is demonstrated to show the effectiveness of the proposed model.

On-line tuning of a single EWMA controller based on the neural technique

The exponentially weighted moving average (EWMA) controller has been proven to be an effective algorithm in the control the modern manufacturing system. The performance of the EWMA controlled process is based on choosing the correct EWMA gain. Most related research has focused on analysing the optimal EWMA gain in the static condition. The objective was to propose an approach based on the neural technique for on-line tuning of the single EWMA gain. The underlying approach indicated that the network learns very quickly when taking autocorrelation function and sample partial autocorrelation function patterns as the input features. It is shown that the sequence of the EWMA gains, generated by the proposed adaptive approach, converges close to the optimal controller value under several disturbance models, including IMA(1,1), and step and small ramp disturbances. In addition, the approach possesses a superior controlled output performance compared with the previous adaptive system.

MDS: a novel method for class imbalance learning

Lots of real-world data sets have imbalanced class distributions in which almost all examples belong to one class and far fewer instances belong to others. Compared with the majority examples, the minority examples are usually more interesting class, such as rare diseases in diagnosis data, failures in inspection data, frauds in credit screening data, and so on. A classifier induced from an imbalanced data set has high classification accuracy for the majority class, but an unacceptable error rate for the minority class. This situation is called class imbalance problem and has attracted lots of attentions of researchers in data mining area. To solve this problem, this work proposed a novel method, called Mahalanobis Distance based sampling (MDS) methodology. Experimental results indicated the proposed MDS have a better performance in identifying the minority class compared with traditional techniques, under-sampling, cost-adjusting, and cluster based sampling.

CUSTOMER SEGMENTATION AND CLASSIFICATION FROM BLOGS BY USING DATA MINING: AN EXAMPLE OF VOIP PHONE

Blogs have been considered the 4th Internet application that can cause radical changes in the world, after e-mail, instant messaging, and Bulletin Board System (BBS). Many Internet users rely heavily on them to express their emotions and personal comments on whatever topics interest them. Nowadays, blogs have become the popular media and could be viewed as new marketing channels. Depending on the blog search engine, Technorati, we tracked about 94 million blogs in August 2007. It also reported that a whole new blog is created every 7.4 seconds and 275,000 blogs are updated daily. These figures can be used to illustrate the reason why more and more companies attempt to discover useful knowledge from this vast number of blogs for business purposes. Therefore, blog mining could be a new trend of web mining. The major objective of this study is to present a structure that includes unsupervised (self-organizing map) and supervised learning methods (back-propagation neural networks, decision tree, and support vector machines) for extracting knowledge from blogs, namely, a blog mining (BM) model. Moreover, a real case regarding VoIP (Voice over Internet Protocol) phone products is provided to demonstrate the effectiveness of the proposed method.