Steel Plate Faults Diagnosis with S-MTS (S-MTS를 이용한 강판의 표면 결함 진단)
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- Journal of Intelligence and Information Systems
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- v.23 no.1
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- pp.47-67
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- 2017
Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.
1. The 'Kao Zheng Pai(考證派) comes from the 'Zhe Zhong Pai' and is a school that is influenced by the confucianism of the Qing dynasty. In Japan Inoue Kinga(井上金娥), Yoshida Koton(吉田篁墩) became central members, and the rise of the methodology of historical research(考證學) influenced the members of the 'Zhe Zhong Pai', and the trend of historical research changed from confucianism to medicine, making a school of medicine based on the study of texts and proving that the classics were right. 2. Based on the function of 'Nei Qu Li '(內驅力) the 'Kao Zheng Pai', in the spirit of 'use confucianism as the base', researched letters, meanings and historical origins. Because they were influenced by the methodology of historical research(考證學) of the Qing era, they valued the evidential research of classic texts, and there was even one branch that did only historical research, the 'Rue Xue Kao Zheng Pai'(儒學考證派). Also, the 'Yi Xue Kao Zheng Pai'(醫學考證派) appeared by the influence of Yoshida Kouton and Kariya Ekisai(狩谷掖齋). 3. In the 'Kao Zheng Pai(考證派)'s theories and views the 'Yi Xue Kao Zheng Pai' did not look at medical scriptures like the "Huang Di Nei Jing"("黃帝內經") and did not do research on 'medical' related areas like acupuncture, the meridian and medicinal herbs. Since they were doctors that used medicine, they naturally were based on 'formulas'(方劑) and since their thoughts were based on the historical ideologies, they valued the "Shang Han Ja Bing Lun" which was revered as the 'ancestor of all formulas'(衆方之祖). 4. The lives of the important doctors of the 'Kao Zheng Pai' Meguro Dotaku(目黑道琢) Yamada Seichin(山田正珍), Yamada Kyoko(山田業廣), Mori Ritsi(森立之) Kitamura Naohara(喜多村直寬) are as follows. 1) Meguro Dotaku(目黑道琢 1739
1.The 'Kao Zheng Pai'(考證派) comes from the 'Zhe Zhong Pai(折衷派)' and is a school that is influenced by the confucianism of the Qing dynasty. In Japan Inoue Kinga(井上金峨), Yoshida Koton(古田篁墩