检索结果-内蒙古大学图书馆

control chart patterns Recognition Based on Optimized Deep Belief Neural Network and Data Information Enhancement

IEEE ACCESS 2020年 8卷 203685-203699页

作者： Chu, Hongyan Zhao, Kailin Cheng, Qiang Li, Rui Yang, Congbin Beijing Univ Technol Inst Adv Mfg & Intelligent Technol Beijing 100124 Peoples R China Beijing Key Lab Adv Mfg Technol Beijing 100124 Peoples R China Machinery Ind Key Lab Heavy Machine Tool Digital Beijing 100124 Peoples R China

control chart patterns (CCPs) are often used for quality control in the manufacturing process, and effective recognition of these patterns is critical to manufacturing. In the dynamic production process, the raw data and features of CCPs are used to recognize or further predict the trends. However, the inaccuracy of CCPs information extraction, loss of information, and complex recognizer can lead to the difficulty of recognition. In order to improve the accuracy of information extraction and recognition, a CCPs recognition method based on optimized deep belief network (DBN) and data information enhancement was proposed. Adaptive features selection and information enhancement (AFIE) was used to select the most appropriate features and make these features combine with the raw data to from the dataset in order to reduce the data dimension, and then combine dimensioned data with the selected features to enhance the data information. Further, this study presented a DBN with three restricted Boltzmann machine structures, which was optimized by using the artificial fish swarm algorithm (AFSA). The method of AFIE was discussed to obtain the optimal data set, and parameters of the network structure were analyzed, optimized, and discussed based on experiments and AFSA. At the same time, this method was compared with multi-layer perceptron neural network. The simulation results showed that the method proposed in this study exhibited excellent effect, and the recognition accuracy achieved by this method was 99.78% for 2000 samples of each pattern.

关键词： control chart patterns adaptive feature selection and information enhancement artificial fish swarm algorithm deep belief network

来源：评论

学校读者我要写书评

暂无评论

Convolutional Neural Network for control chart patterns Recognition 19

Convolutional Neural Network for Control Chart Patterns Reco...

引用

3rd International Conference on Computer Science and Application Engineering (CSAE)

作者： Hong, Zhiyong Li, Yuqiang Zeng, Zhiqiang Wuyi Univ Fac Intelligent Mfg Jiangmen Guangdong Peoples R China

ISBN: (纸本)9781450362948

control chart, as a basic tool of statistical process control (SPC), has been widely used in monitoring the fluctuation of variables in manufacturing processes. Unnatural control chart patterns (CCPs) are associated with specific causes which can help practitioners control the manufacturing processes. Over the last several decades, many machine learning methods have been applied to the control chart pattern recognition, and have obtained outstanding results. Convolutional neural networks (CNNs), as a state-of-the-art deep machine learning approach, have been demonstrated remarkable performance in many machine learning fields. In this paper, a novel convolutional neural network was proposed to recognize CCPs. Different from traditional classification methods need hand-crafted features which will need a great engineering effort to improve the classifier accuracy, the proposed method extracts multilevel features from input data automatically. Besides, different kind of basic CPPs and concurrent CCPs (two or more basic CCPs occurring simultaneously) recognition model have been investigated by many researchers, but most models would start from scratch and the previous ones would be abandoned when a new CCPs recognition model was built in the research. To solve this problem, the strategy of transfer learning was used in a finely-tuned previous model to accomplish a new CPPs recognition task. The experiment showed that the proposed CNN model outperformed the support vector machine (SVM) model, and that building a CCPs classifier based on transfer learning has a higher performance than building a classifier from scratch.

关键词： control chart patterns Concurrent patterns CNN Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Recognition of control chart patterns using an intelligent technique

引用

APPLIED SOFT COMPUTING 2013年第5期13卷 2970-2980页

作者： Ebrahimzadeh, Ata Addeh, Jalil Ranaee, Vahid Babol Univ Technol Fac Elect & Comp Engn Babol Sar Iran

control chart patterns (CCPs) are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in the manufacturing processes. This paper presents a novel hybrid intelligent method for recognition of common types of CCP. The proposed method includes three main modules: the feature extraction module, the classifier module and optimization module. In the feature extraction module, a proper set of the shape features and statistical features is proposed as the efficient characteristic of the patterns. In the classifier module multilayer perceptron neural network and support vector machine (SVM) are investigated. In support vector machine training, the hyper-parameters have very important roles for its recognition accuracy. Therefore, in the optimization module, improved bees algorithm is proposed for selecting of appropriate parameters of the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy. (C) 2012 Elsevier B. V. All rights reserved.

关键词： control chart patterns Neural networks Shape features Statistical feature Support vector machine

来源：评论

学校读者我要写书评

暂无评论

Application of integrated recurrent neural network with multivariate adaptive regression splines on SPC-EPC process

引用

JOURNAL OF MANUFACTURING SYSTEMS 2020年 57卷 109-118页

作者： Kao, Ling-Jing Chiu, Chih Chou Natl Taipei Univ Technol Dept Business Management Taipei Taiwan

The integration of statistical process control and engineering process control has been reported as an effective way to monitor and control the autocorrelated process. However, because engineering process control compensates for the effects of underlying disturbances, the disturbance patterns become very hard to recognize, especially when various abnormal control chart patterns are mixed and co-existed in the engineering process. In this study, a new control chart pattern recognition model which integrates multivariate adaptive regression splines and recurrent neural network is proposed to not only address the problem of feature selection (i.e., lagged process measurements) but also improve the pattern recognition accuracy. The performance of the proposed method is evaluated by comparing the recognition results of multivariate adaptive regression splines and recurrent neural network with the results of four competing approaches (multivariate adaptive regression splines extreme learning machine, multivariate adaptive regression splines-random forest, single recurrent neural network, and single random forest) on the simulated individual process data. The experimental study shows that the proposed multivariate adaptive regression splines and recurrent neural network approach can not only solve the problem of variable selection but also outperform other competing models. Moreover, according to the lagged process measurements selected by the proposed approach, lagged observations that exerted significant impact on the construction of the control chart pattern recognition model can be identified successfully. This study has significant implications for research and practice in production management and provides a valuable reference for manufacturing process managers to better understand and develop strategies for control chart pattern recognition.

关键词： Statistical process control Engineering process control control chart patterns Recurrent neural network Multivariate adaptive regression splines

来源：评论

学校读者我要写书评

暂无评论

On-line concurrent control chart pattern recognition using singular spectrum analysis and random forest

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2021年 159卷 107538-107538页

作者： Chiu, Jing-Er Tsai, Cheng-Han Natl Yunlin Univ Sci & Technol Dept Ind Engn & Management Touliu 64002 Yunlin Taiwan

A scheme integrated with engineer process control (EPC) is a useful approach to identify the disturbances for statistical process control (SPC) charting of an auto-correlated process. Although the EPC is able to compensate for the underlying disturbance, the underlying disturbance is embedded in the control chart. Thus, this makes the control chart patterns (CCPs) difficult to be identified. As documented in the literature, much efforts have been put in the recognition of single basic patterns of unnatural variation. However, there could exist a combination of two unnatural patterns simultaneously in a real-world manufacturing process. Also for an automated real-time production line, data are collected automatically and monitored by a computer-based system. Thus, the early detection of abnormality is of high importance for the aforesaid problem. This study aims to establish an on-line detection system used for monitoring the mixture unnatural CCPs for a SPC-EPC process. This paper presents a hybrid approach based on singular spectrum analysis (SSA) and random forest (RF) to identify the concurrent CCPs in an on-line SPC-EPC process. A total of fifteen types of concurrent CCPs were utilized to validate the proposed method. The SSA method was also used to decompose the mixture patterns into single patterns. The RF was employed to identify the types of patterns to which it belongs. The results showed that the proposed method was able to handle most of the concurrent CCPs very successfully with an average accurate identification rate of 91.8%. Also, the proposed method was found to be more accurate and efficient than the use of the hybrid method of SSA and support vector machine (SVM). It is suggested that this proposed system could be possibly applied for monitoring an on-line process to a greater extent.

关键词： Auto-correlated process SPC-EPC process control chart patterns Singular spectrum analysis Random forest

来源：评论

学校读者我要写书评

暂无评论

The Method of Determination and Detection of Abnormal States in a GT8C Turbine

引用

TEHNICKI VJESNIK-TECHNICAL GAZETTE 2020年第3期27卷 725-731页

作者： Stefanowicz, Konrad Balasz, Blazej Lipinski, Dariusz Wlazewski, Grzegorz Jacob Paradies Univ AJP Ul Teatralna 25 PL-66400 Gorzow Wielkopolski Poland Koszalin Univ Technol Ul Raclawicka 15-17 PL-75620 Koszalin Poland

The paper describes a new method of determining abnormal states and the way they are detected on GT8C turbine. The system of dividing measured parameters into three groups has been suggested. The paper puts the emphasis on the issue of identifying relevant information contained in the data set. Properties of the GT8C have been given. The significance of monitoring the essential operating parameters has been noted. The steps leading towards the detection of anomalies related to the measured parameter have been made. The tool in form of the method for determining abnormal states shows how to easily implement and supplement the supervision over measured parameters of the machine. The use of computer assisted methods dramatically accelerates the analysis of basic components of the measured parameters thus facilitating the possibilities of detecting the abnormal state of the machine and making a decision on the response.

关键词： control chart patterns gas turbine pattern recognition prediction

来源：评论

学校读者我要写书评

暂无评论

Mixture control chart patterns recognition using independent component analysis and support vector machine

引用

NEUROCOMPUTING 2011年第11期74卷 1908-1914页

作者： Lu, Chi-Jie Shao, Yuehjen E. Li, Po-Hsun Ching Yun Univ Dept Ind Engn & Management Tao Yuan 320 Taiwan Fu Jen Catholic Univ Dept Stat & Informat Sci Hsinchuang 242 Taipei County Taiwan Fu Jen Catholic Univ Grad Inst Appl Stat Hsinchuang 242 Taipei County Taiwan

Effective recognition of control chart patterns (CCPs) is an important issue since abnormal patterns exhibited in control charts can be associated with certain assignable causes which affect the process. Most of the existing studies assume that the observed process data which needs to be recognized are basic types of abnormal CCPs. However, in practical situations, the observed process data could be mixture patterns, which consist of two basic CCPs combined together. In this study, a hybrid scheme using independent component analysis (ICA) and support vector machine (SVM) is proposed for CCPs recognition. The proposed hybrid ICA-SVM scheme initially applies an ICA to the mixture patterns in order to generate independent components (ICs). The hidden basic patterns of the mixture patterns can be discovered in these ICs. The ICs can then serve as the input variables of the SVM for building a CCP recognition model. Experimental results revealed that the proposed scheme is able to effectively recognize mixture control chart patterns and outperform the single SVM models, which did not use an ICA as a preprocessor. (C) 2011 Elsevier B.V. All rights reserved.

关键词： control chart patterns Independent component analysis Support vector machine Pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Fuzzy logic based assignable cause diagnosis using control chart patterns

引用

INFORMATION SCIENCES 2010年第17期180卷 3258-3272页

作者： Demirli, Kudret Vijayakumar, Sujikumar Concordia Univ Fuzzy Syst Res Lab Dept Mech & Ind Engn Montreal PQ H3G 1M8 Canada

control chart patterns, besides determining the presence of assignable causes, also provide hints on the nature of assignable cause(s) present. Relating the patterns exhibited on the control chart to assignable causes is an ambiguous and vague task especially when multiple patterns co-exist. In this work, a rule based fuzzy inference system is developed for X control chart to prioritize the control chart causes based on the accumulated evidence. When a process goes out of control, search for assignable causes can be assisted by the priorities assigned to the causes. For an in-control process, developing patterns can be tracked and preventive action can be taken to prevent the process from going out of control. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Fuzzy statistical process control control chart patterns Assignable cause diagnosis

来源：评论

学校读者我要写书评

暂无评论

Recognition of control chart patterns using improved selection of features

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2009年第4期56卷 1577-1588页

作者： Gauri, Susanta Kumar Chakraborty, Shankar Jadavpur Univ Dept Prod Engn Kolkata 700032 India Indian Stat Inst SQC & OR Unit Kolkata 700108 India

Recognition of various control chart patterns (CCPs) can significantly reduce the diagnostic search process. Feature-based approaches can facilitate efficient pattern recognition. The full potentiality of feature-based approaches can be achieved by using the optimal set of features. In this paper, a set of seven most useful features is selected using a classification and regression tree (CART)-based systematic approach for feature selection. Based on these features, eight most commonly observed CCPs are recognized using heuristic and artificial neural network (ANN) techniques. Extensive performance evaluation of the two types of recognizers reveals that both these recognizers result in higher recognition accuracy than the earlier reported feature-based recognizers. In this work, various features are extracted from the control chart plot of actual process data in such a way that their values become independent of the process mean and standard deviation. Thus, the developed feature-based CCP recognizers can be applicable to any general process. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： control chart patterns Shape features Heuristics Artificial neural network Pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Statistical process control using optimized neural networks: A case study

引用

ISA TRANSACTIONS 2014年第5期53卷 1489-1499页

作者： Addeh, Jalil Ebrahimzadeh, Ata Azarbad, Milad Ranaee, Vahid Bargh Costar Baharan Golestan Corp Gonbad Kavus Iran Babol Noushirvani Univ Technol Fac Elect & Comp Engn Babol Sar Iran

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. (C) 2013 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： control chart patterns COA Neural networks Shape feature Statistical feature

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：