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Wei L, Zhai B, Sun H, Hu Z, Zhao Z. An ensemble JITL method based on multi-weighted similarity measures for cold rolling force prediction. ISA TRANSACTIONS 2022; 126:326-337. [PMID: 34334182 DOI: 10.1016/j.isatra.2021.07.030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 07/18/2021] [Accepted: 07/19/2021] [Indexed: 06/13/2023]
Abstract
In the cold tandem rolling process, the product quality and yield are affected by the accuracy of rolling force prediction directly. Fix prediction model is not applicable to the multi-operating conditions rolling environment. In addition, appropriate samples can be hardly selected by a single similarity measure because of the insufficient process knowledge. In order to solve these issues, an ensemble just-in-time-learning modeling method based on multi-weighted similarity measures (MWS-EJITL) is proposed. Firstly, multi-weighted similarity measures is used to select relevant samples. Then, the local model is constructed and the output value of the query data is estimated. Finally, the ensemble learning strategy is adopted to integrate the outputs of each local model. On this basis, the cumulative similarity factor is introduced to optimize the number of samples of local modeling, and the similarity threshold is set to update the local model adaptively. The rolling force prediction experiment verify the effectiveness and accuracy of MWS-EJITL method.
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Affiliation(s)
- Lixin Wei
- Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, China
| | - Bohao Zhai
- Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, China
| | - Hao Sun
- Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, China.
| | - Ziyu Hu
- Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, China
| | - Zhiwei Zhao
- Department of Computer Science and Technology, Tangshan University, Tangshan, China
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Liu K, Shao W, Chen G. Autoencoder-based nonlinear Bayesian locally weighted regression for soft sensor development. ISA TRANSACTIONS 2020; 103:143-155. [PMID: 32171594 DOI: 10.1016/j.isatra.2020.03.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 03/07/2020] [Accepted: 03/09/2020] [Indexed: 06/10/2023]
Abstract
The framework of locally weighted learning (LWL) has established itself as a popular tool for developing nonlinear soft sensors in process industries. For LWL-based soft sensors, the key factor for achieving high performance is to construct accurate localized models. To this end, in this paper a nonlinear local model training algorithm called nonlinear Bayesian weighted regression (NBWR) is proposed. In the NBWR, the nonlinear features of process data are first extracted by the autoencoder; then, given a query sample a local dataset is selected on the feature space and a fully Bayesian regression model with differentiated sample weights is developed. The benefits of this approach, which include better consistency of correlation, stronger abilities to deal with process nonlinearities and uncertainties, overfitting and numerical issues, lead to superior performance. The NBWR is used for developing a soft sensor under the LWL framework, and a real-world industrial process is used to evaluate the performance of the NBWR-based soft sensor. The experimental results demonstrate that the proposed method outperforms several benchmarking soft sensing approaches.
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Affiliation(s)
- Kang Liu
- Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, China.
| | - Weiming Shao
- College of New Energy, China University of Petroleum, Qingdao 266580, China.
| | - Guoming Chen
- Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, China.
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Sun K, Tian P, Qi H, Ma F, Yang G. An Improved Normalized Mutual Information Variable Selection Algorithm for Neural Network-Based Soft Sensors. SENSORS 2019; 19:s19245368. [PMID: 31817459 PMCID: PMC6960561 DOI: 10.3390/s19245368] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 11/24/2019] [Accepted: 12/02/2019] [Indexed: 11/28/2022]
Abstract
In this paper, normalized mutual information feature selection (NMIFS) and tabu search (TS) are integrated to develop a new variable selection algorithm for soft sensors. NMIFS is applied to select influential variables contributing to the output variable and avoids selecting redundant variables by calculating mutual information (MI). A TS based strategy is designed to prevent NMIFS from falling into a local optimal solution. The proposed algorithm performs the variable selection by combining the entropy information and MI and validating error information of artificial neural networks (ANNs); therefore, it has advantages over previous MI-based variable selection algorithms. Several simulation datasets with different scales, correlations and noise parameters are implemented to demonstrate the performance of the proposed algorithm. A set of actual production data from a power plant is also used to check the performance of these algorithms. The experiments showed that the developed variable selection algorithm presents better model accuracy with fewer selected variables, compared with other state-of-the-art methods. The application of this algorithm to soft sensors can achieve reliable results.
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Affiliation(s)
- Kai Sun
- School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China (F.M.)
- Correspondence: (K.S.); (G.Y.); Tel.: +86-15269190537 (K.S.); +86-13651869523 (G.Y.)
| | - Pengxin Tian
- School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China (F.M.)
| | - Huanning Qi
- School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China (F.M.)
| | - Fengying Ma
- School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China (F.M.)
| | - Genke Yang
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
- Ningbo Artificial Intelligence Institute, Shanghai Jiao Tong University, Ningbo 315000, China
- Correspondence: (K.S.); (G.Y.); Tel.: +86-15269190537 (K.S.); +86-13651869523 (G.Y.)
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4
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Soft Sensing of Silicon Content via Bagging Local Semi-Supervised Models. SENSORS 2019; 19:s19173814. [PMID: 31484466 PMCID: PMC6749592 DOI: 10.3390/s19173814] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 09/01/2019] [Accepted: 09/02/2019] [Indexed: 11/17/2022]
Abstract
The silicon content in industrial blast furnaces is difficult to measure directly online. Traditional soft sensors do not efficiently utilize useful information hidden in process variables. In this work, bagging local semi-supervised models (BLSM) for online silicon content prediction are proposed. They integrate the bagging strategy, the just-in-time-learning manner, and the semi-supervised extreme learning machine into a unified soft sensing framework. With the online semi-supervised learning method, the valuable information hidden in unlabeled data can be explored and absorbed into the prediction model. The application results to an industrial blast furnace show that BLSM has better prediction performance compared with other supervised soft sensors.
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He Y, Zhu B, Liu C, Zeng J. Quality-Related Locally Weighted Non-Gaussian Regression Based Soft Sensing for Multimode Processes. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b04075] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Yuchen He
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
| | - Binbin Zhu
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
| | - Chenyang Liu
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
| | - Jiusun Zeng
- College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
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Pan D, Jiang Z, Chen Z, Gui W, Xie Y, Yang C. Temperature Measurement Method for Blast Furnace Molten Iron Based on Infrared Thermography and Temperature Reduction Model. SENSORS 2018; 18:s18113792. [PMID: 30404156 PMCID: PMC6263440 DOI: 10.3390/s18113792] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Revised: 11/01/2018] [Accepted: 11/02/2018] [Indexed: 11/30/2022]
Abstract
The temperature measurement of blast furnace (BF) molten iron is a mandatory requirement in the ironmaking process, and the molten iron temperature is significant in estimating the molten iron quality and control blast furnace condition. However, it is not easy to realize real-time measurement of molten iron temperature because of the harsh environment in the blast furnace casthouse and the high-temperature characteristics of molten iron. To achieve continuous detection of the molten iron temperature of the blast furnace, this paper proposes a temperature measurement method based on infrared thermography and a temperature reduction model. Firstly, an infrared thermal imager is applied to capture the infrared thermal image of the molten iron flow after the skimmer. Then, based on the temperature distribution of the molten iron flow region, a temperature mapping model is established to measure the molten iron temperature after the skimmer. Finally, a temperature reduction model is developed to describe the relationship between the molten iron temperature at the taphole and skimmer, and the molten iron temperature at the taphole is calculated according to the temperature reduction model and the molten iron temperature after the skimmer. Industrial experiment results illustrate that the proposed method can achieve simultaneous measurement of molten iron temperature at the skimmer and taphole and provide reliable temperature data for regulating the blast furnace.
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Affiliation(s)
- Dong Pan
- School of Information Science and Engineering, Central South University, Changsha 410083, China.
| | - Zhaohui Jiang
- School of Information Science and Engineering, Central South University, Changsha 410083, China.
| | - Zhipeng Chen
- School of Information Science and Engineering, Central South University, Changsha 410083, China.
| | - Weihua Gui
- School of Information Science and Engineering, Central South University, Changsha 410083, China.
| | - Yongfang Xie
- School of Information Science and Engineering, Central South University, Changsha 410083, China.
| | - Chunhua Yang
- School of Information Science and Engineering, Central South University, Changsha 410083, China.
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Zhang Y, Yang X, Shardt YAW, Cui J, Tong C. A KPI-Based Probabilistic Soft Sensor Development Approach that Maximizes the Coefficient of Determination. SENSORS 2018; 18:s18093058. [PMID: 30213097 PMCID: PMC6163717 DOI: 10.3390/s18093058] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Revised: 08/27/2018] [Accepted: 09/07/2018] [Indexed: 12/28/2022]
Abstract
Advanced technology for process monitoring and fault diagnosis is widely used in complex industrial processes. An important issue that needs to be considered is the ability to monitor key performance indicators (KPIs), which often cannot be measured sufficiently quickly or accurately. This paper proposes a data-driven approach based on maximizing the coefficient of determination for probabilistic soft sensor development when data are missing. Firstly, the problem of missing data in the training sample set is solved using the expectation maximization (EM) algorithm. Then, by maximizing the coefficient of determination, a probability model between secondary variables and the KPIs is developed. Finally, a Gaussian mixture model (GMM) is used to estimate the joint probability distribution in the probabilistic soft sensor model, whose parameters are estimated using the EM algorithm. An experimental case study on the alumina concentration in the aluminum electrolysis industry is investigated to demonstrate the advantages and the performance of the proposed approach.
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Affiliation(s)
- Yue Zhang
- Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
| | - Xu Yang
- Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
| | - Yuri A W Shardt
- Department of Automation Engineering, Technical University of Ilmenau, 98684 Ilmenau, Thuringia, Germany.
| | - Jiarui Cui
- Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
| | - Chaonan Tong
- Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
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