检索结果-内蒙古大学图书馆

第三十九届中国控制会议

作者： Jian Tang Hongjuan Zhu Dong Li Jian Zhang Gang Yu Faculty of Information Technology Beijing University of Technology PLA Navy 55th Institute Joint staff of the Central Military Commission College of computer and software Nanjing University of Information Science and Technology State (Beijing) Key Laboratory of Process Automation in Mining & Metallurgy

The mapping relationship between the mill load and the multi-component mechanical signals generated by the ball mill of the mineral grinding process is non-deterministic and complex. With the inherent filtering function of the human ear, the operating expert can effectively estimate the mill load and its internal parameters for their familiar mill in the actual industrial process. In order to obtain multiple single-mode sub-signals with physical meaning and complementary characteristic, this paper proposes a single-mode sub-signal selection method based on variational modal decomposition（VMD） and predictive performance. At first, based on prior knowledge, the value of decomposition layers required to perform VMD is determined. Then, VMD is used to decompose the original mechanical signal into multiple time-domain single-mode sub-signals with different bandwidths and time scales, and further are transformed to the frequency domain to obtain candidate single-mode sub-signal frequency spectrum. Finally, based on these candidate spectral data, a serial of candidate sub-models for mill load parameter prediction are constructed, and a series of selective ensemble models are built for obtaining reduced single-mode sub-signal frequency spectrum, The final single-mode sub-signals with the biggest complementary characteristics are selected based on the practical requirement. The effectiveness of the method is demonstrated by comparative experiment simulations based on the shell vibration signal of a laboratory-scale ball mill.

关键词： Variational modal decomposition（VMD） decomposition layer single-mode sub-signal prediction performance

来源：评论

学校读者我要写书评

暂无评论

Mechanical Frequency Spectrum Sub-band Feature Estimation Based on Multiple Types Correlation Analysis

Mechanical Frequency Spectrum Sub-band Feature Estimation Ba...

引用

第三十九届中国控制会议

作者： Jian Tang Meiying Jia Fupeng Wang Jian Zhang Gang Yu Faculty of Information Technology Beijing University of Technology 55th Institute Joint staff of the Central Military Commission 91411 Unit PLA College of computer and software Nanjing University of Information Science and Technology State Key Laboratory of Process Automation in Mining & Metallurgy

Mill load is one of the key factors that affects the operation optimization and control of grinding process. It is difficult to detect accurately in real time. The mill load relates to multiple channel mechanical signals, such as the mill shell vibration signals, the shaft seat front and rear vibration signals, the acoustic signals near to the shell surface and under the grinding area, *** to evaluate these sub-band feature of the mechanical spectrum of the above channels is a difficult problem. It can help to clarify the ball mill grinding mechanism and the mechanical signal generation mechanism. To solve these problems, this paper proposes a multi-channel mechanical frequency spectrum sub-band feature evaluation method based on multiple types correlation analysis. Firstly, the multi-channel mechanical signals are transformed from time domain to frequency domain to obtain the frequency spectrum data, which are divided into several sub-bands to extract features. Then, the normalized correlation coefficients between the multi-channel sub-band feature and the mill load are calculated. Thirdly, based on the partial least square algorithm, the variable projection importance（VIP） value of sub-band features are also calculated. The VIPs are normalized same as that of correlation coefficient. Finally, a new index is calculated by using normalized correlation coefficient and VIP value and the model’s prediction performance. These sub-band features of single channel and all channel mechanical signals are measured based on the new defined combined evaluation index. The multi-channel mechanical signals of a laboratory-scale ball mill are used to verify the effectiveness of the proposed method.

关键词： Multiple channel mechanical signals mechanical frequency spectrum sub-band features correlation coefficient variable projection importance（VIP）

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to “Simultaneous and clean separation of titanium, iron, and alumina from coal fly ash in one spot: Electrolysis-hydrolysis method” [Sep. Purif. Technol. 294 (2022) 121247]

引用

Separation and Purification Technology 2022年 298卷

作者： Yuan Shi Kai-xi Jiang Ting-an Zhang Xiao-feng Zhu State Key Laboratory of Advanced Processing and Recycling of Nonferrous Metals Lanzhou University of Technology Lanzhou 730050 China College of Zijin Mining Fuzhou University Fuzhou 350000 China Key Laboratory of Ecological Metallurgy of Multi-metal Intergrown Ores of Ministry of Education Special Metallurgy and Process Engineering Institute School of Metallurgy Northeastern University Shenyang 110819 China

来源：评论

学校读者我要写书评

暂无评论

RVFLN-based online adaptive semi-supervised learning algorithm with application to product quality estimation of industrial processes

引用

Journal of Central South University 2019年第12期26卷 3338-3350页

作者： DAI Wei HU Jin-cheng CHENG Yu-hu WANG Xue-song CHAI Tian-you School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

Direct online measurement on product quality of industrial processes is difficult to be realized,which leads to a large number of unlabeled samples in modeling ***,it needs to employ semi-supervised learning(SSL)method to establish the soft sensor model of product *** the slow time-varying characteristic of industrial processes,the model parameters should be updated *** to this characteristic,this paper proposes an online adaptive semi-supervised learning algorithm based on random vector functional link network(RVFLN),denoted as *** introducing a L2-fusion term that can be seen a weight deviation constraint,the proposed algorithm unifies the offline and online learning,and achieves smoothness of model parameter *** evaluations both on benchmark testing functions and datasets reveal that the proposed OAS-RVFLN can outperform the conventional methods in learning speed and ***,the OAS-RVFLN is applied to the coal dense medium separation process in coal industry to estimate the ash content of coal product,which further verifies its effectiveness and potential of industrial application.

关键词： semi-supervised learning(SSL) L2-fusion term online adaptation random vector functional link network(RVFLN)

来源：评论

学校读者我要写书评

暂无评论

Soft Measuring Method of Pcocess Parameter Based on Multiple Feature Subsets

Soft Measuring Method of Pcocess Parameter Based on Multiple...

引用

Chinese automation Congress (CAC)

作者： Zhuo Liu Jian Tang Gang Yu YuChen Sun State Key Laboratory of Synthetical Automation for Process Industries Northeaster University Shenyang China Faculty of Information Technology Beijing University of Technology Beijing China State Key Laboratory (Beijing Key Laboratory) of Process Automation in Mining & Metallurgy Beijing China Academy of Mathematics and System Science Chinese Academy of Sciences Beijing China

Difficulty-to-measure process parameter relative to production quality and efficient of complex industrial process is obtained normally by off-line analysis or expert estimation. One of the main reason is that the soft measuring model between multi-source input features and such process parameter is difficulty to be constructed. Aim at the above issue, a new soft measuring method is proposed in this study. At first, linear and nonlinear feature sub-sets are selected by using correlation coefficient and mutual information method. Then, four types of linear and nonlinear candidate sub-models are constructed based on the above feature subsets. At last, optimization and weighting algorithms are used to select and combine the selected ensemble sub-models. Thus, the final selective ensemble learning-based soft measuring model is obtained. The modeling results based on high dimensional mechanical vibration frequency spectrum validate effectiveness of this approach.

关键词： Feature extraction Correlation coefficient Predictive models Mutual information Data models Optimization automation

来源：评论

学校读者我要写书评

暂无评论

Mechanical Frequency Spectrum Sub-band Feature Estimation Based on Multiple Types Correlation Analysis

Mechanical Frequency Spectrum Sub-band Feature Estimation Ba...

引用

Chinese Control Conference (CCC)

作者： Jian Tang Meiying Jia Fupeng Wang Jian Zhang Gang Yu Faculty of Information Technology Beijing University of Technology Beijing China Joint staff of the Central Military Commission 55th Institute Beijing China 91411 Unit PLA Dalian China College of computer and software Nanjing University of Information Science and Technology Nanjing China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Mill load is one of the key factors that affects the operation optimization and control of grinding process. It is difficult to detect accurately in real time. The mill load relates to multiple channel mechanical signals, such as the mill shell vibration signals, the shaft seat front and rear vibration signals, the acoustic signals near to the shell surface and under the grinding area, etc. How to evaluate these sub-band feature of the mechanical spectrum of the above channels is a difficult problem. It can help to clarify the ball mill grinding mechanism and the mechanical signal generation mechanism. To solve these problems, this paper proposes a multi-channel mechanical frequency spectrum sub-band feature evaluation method based on multiple types correlation analysis. Firstly, the multi-channel mechanical signals are transformed from time domain to frequency domain to obtain the frequency spectrum data, which are divided into several sub-bands to extract features. Then, the normalized correlation coefficients between the multi-channel sub-band feature and the mill load are calculated. Thirdly, based on the partial least square algorithm, the variable projection importance (VIP) value of sub-band features are also calculated. The VIPs are normalized same as that of correlation coefficient. Finally, a new index is calculated by using normalized correlation coefficient and VIP value and the model's prediction performance. These sub-band features of single channel and all channel mechanical signals are measured based on the new defined combined evaluation index. The multi-channel mechanical signals of a laboratory-scale ball mill are used to verify the effectiveness of the proposed method.

关键词： Vibrations Feature extraction Correlation coefficient Shafts Acoustics Electronic mail Frequency-domain analysis

来源：评论

学校读者我要写书评

暂无评论

Single-mode Sub-signal Selection Method Based on VMD and Predic Performance for Multi-component Mechanical Signal

Single-mode Sub-signal Selection Method Based on VMD and Pre...

引用

Chinese Control Conference (CCC)

作者： Jian Tang Hongjuan Zhu Dong Li Jian Zhang Gang Yu Faculty of Information Technology Beijing University of Technology Beijing China PLA Navy Beijing China 55th Institute Joint staff of the Central Military Commission Beijing China College of computer and software Nanjing University of Information Science and Technology Nanjing China State (Beijing) Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

The mapping relationship between the mill load and the multi-component mechanical signals generated by the ball mill of the mineral grinding process is non-deterministic and complex. With the inherent filtering function of the human ear, the operating expert can effectively estimate the mill load and its internal parameters for their familiar mill in the actual industrial process. In order to obtain multiple single-mode sub-signals with physical meaning and complementary characteristic, this paper proposes a single-mode sub-signal selection method based on variational modal decomposition (VMD) and predictive performance. At first, based on prior knowledge, the value of decomposition layers required to perform VMD is determined. Then, VMD is used to decompose the original mechanical signal into multiple time-domain single-mode sub-signals with different bandwidths and time scales, and further are transformed to the frequency domain to obtain candidate single-mode sub-signal frequency spectrum. Finally, based on these candidate spectral data, a serial of candidate sub-models for mill load parameter prediction are constructed, and a series of selective ensemble models are built for obtaining reduced single-mode sub-signal frequency spectrum, The final single-mode sub-signals with the biggest complementary characteristics are selected based on the practical requirement. The effectiveness of the method is demonstrated by comparative experiment simulations based on the shell vibration signal of a laboratory-scale ball mill.

关键词： Vibrations Time-domain analysis Predictive models Time-frequency analysis Prediction algorithms Electronic mail Minerals

来源：评论

学校读者我要写书评

暂无评论

Research and Application of Nelson Concentrator in Beneficiation Test of Quartz Vein Type Gold Ore

引用

IOP Conference Series: Earth and Environmental Science 2020年第5期546卷

作者： Wenping Zhang Mingming Cai Guangsheng Li Fabo Huang Xingfu Zhu Chao Xu Jianjun Zhao Department Metallurgical Laboratory Branch of Shandong Gold Mining Technology Co. Ltd Yantai 261441 Shandong China Department State Key Laboratory of Process Automation in Mining & Metallurgy

A gold deposit in Shandong belongs to Quartz vein type gold-bearing deposit, the main gold minerals are natural gold and silver-gold minerals, there are a few metallic minerals such as pyrite, the main gangue minerals are quartz, feldspar, sericite and so on, the average size of gold minerals is 62.37 microns, the particle size of the cloth is coarse. As a kind of gravity separation equipment, Nelson concentrator is more and more favored by mines because of its high enrichment ratio, high recovery rate and extremely friendly to environment, the experimental research of Nelson concentrator was carried out, and the gold concentrate with gold grade of 385.06 G / T and recovery of 62.21% was obtained.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Online complex nonlinear industrial process operating optimality assessment using modified robust total kernel partial M-regression

引用

Chinese Journal of Chemical Engineering 2018年第4期26卷 775-785页

作者： Fei Chu Wei Dai Jian Shen Xiaoping Ma Fuli Wang School of Information and Control Engineering China University of Mining and Technology Xuzhou 221116 China State Key Laboratory of Integrated Automation for Process Industries Northeastern University Shenyang 110819 China College of Information Science and Engineering Northeastern University Shenyang 110819 China

Although industrial processes often perform perfectly under design conditions, they may deviate from the optimal operating point owing to parameters drift, environmental disturbances, etc. Thus, it is necessary to develop efficacious strategies or procedure to assess the process performance online. In this paper, we explore the issue of operating optimality assessment for complex industrial processes based on performance-similarity considering nonlinearities and outliers simultaneously, and a general enforced online performance assessment framework is proposed. In the offline part, a new and modified total robust kernel projection to latent structures algorithm,T-KPRM, is proposed and used to evaluate the complex nonlinear industrial process, which can effectively extract the optimal-index-related process variation information from process data and establish assessment models for each performance grades overcoming the effects of outlier. In the online part, the online assessment results can be obtained by calculating the similarity between the online data from a sliding window and each of the performance grades. Furthermore, in order to improve the accuracy of online assessment, we propose an online assessment strategy taking account of the effects of noise and process uncertainties. The Euclidean distance between the sliding data window and the optimal evaluation level is employed to measure the contribution rates of variables, which indicate the possible reason for the non-optimal operating performance. The proposed framework is tested on a real industrial case: dense medium coal preparation process, and the results shows the efficiency of the proposed method comparing to the existing method.

关键词： Performance assessment Optimization Model Economics T-KPRM Robust

来源：评论

学校读者我要写书评

暂无评论

Multi-source Mechanical Signal Analysis Based on Linear Latent Structure Model for Mill Load Parameter Forecasting

Multi-source Mechanical Signal Analysis Based on Linear Late...

引用

第30届中国控制与决策会议

作者： Jian Tang Zhuo Liu Xiaoli Li Gang Yu Jianjun Zhao Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System State Key Laboratory of Synthetical Automation for Process Industries Northeaster University State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Automation in Mining & Metallurgy

Accurate detect mill load inside the heavy rotating ball mill is one of the key factors for realizing operation optimization and control of the mineral grinding process. By using multi-source mechanical signals, such as vibration and acoustical signals at different location of the ball mill system, to construct mill load parameter forecasting(MLPF) model has been a hot and focus recently. However, a few researches about the relative accuracy and reliability contribution ratio of these different multi-source signals are addressed. It is necessary to select the suitable mechanical signal at different industrial applications. Aim to this problem, multi-source mechanical signal analysis based on linear latent structure MLPF model is studied in this paper. At first, Fast Fourier transformer(FFT) is used to obtain mechanical frequency spectrum of the time domain vibration and acoustic signals with characteristics of high dimension and strong co-linearity. Then, project to latent structure(PLS) models are build based on these spectra data. Finally, a new defined metric is used to estimate relative accuracy and reliability contribution ratio of these multi-source signals based on generalization performance and structure parameter of different prediction model. Experiments based on a laboratory-scale ball mill are used to valid the proposed method.

关键词： Mechanical frequency spectrum ball mill mill load parameter forecasting latent structure model

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：