The Experts below are selected from a list of 8376 Experts worldwide ranked by ideXlab platform
Jin Wang - One of the best experts on this subject based on the ideXlab platform.
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Adaptive Outlier Detection and Classification for Online Soft Sensor Update
IFAC Proceedings Volumes, 2016Co-Authors: Hector J Galicia, Q. Peter He, Jin WangAbstract:Abstract Data-driven Soft Sensors that predict the primary variables of a process by using the secondary measurements have drawn increased research interests recently. They are easy to develop and only require a good historical data set. Among them, the partial least squares (PLS) based Soft Sensor is the most commonly used approach for industrial applications. As industrial processes often experience time-varying changes, it is desirable to update the Soft Sensor model with the new process data once the Soft Sensor is implemented online. Because the PLS algorithms are sensitive to outliers in the dataset, outlier detection and handling plays a critical role in the development of the PLS based Soft Sensors. In this work, we develop a multivariate approach for online outlier detection. In addition, to differentiate outliers caused by erroneous readings from those caused by process changes, we propose a Bayesian supervisory approach to analyze and classify the detected outliers. Finally, to address time-varying nature of industrial processes, we proposed a simple yet effective scheme to update the detection threshold. Both simulated and industrial case studies of the Kamyr digesters are used to demonstrate the effectiveness of the proposed approaches.
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a reduced order Soft Sensor approach and its application to a continuous digester
Journal of Process Control, 2011Co-Authors: Hector J Galicia, Peter Q He, Jin WangAbstract:Abstract In many industrial processes, the primary product variable(s) are not measured online but are required for feedback control. To address this challenge, there has been increased interest toward developing data-driven Soft Sensors using secondary measurements based on multivariate regression techniques. Among different data-driven approaches, the dynamic partial least squares (DPLS) Soft Sensor approach has been applied to several industrial processes. However, despite its successful applications, there is a lack of theoretical understanding on the properties of the DPLS Soft Sensor. Specifically, whether it can adequately capture process dynamics and whether it can provide unbiased estimate under closed-loop operation have not been examined rigorously. In this work, we provide a theoretical analysis to answer these questions. In addition, we propose a reduced-order DPLS (RO-DPLS) Soft Sensor approach to address the limitation of the traditional DPLS Soft Sensor when applied to model processes with large transport delay, i.e. , large number of lagged variables are required to be include in the regressor matrix in order to capture process dynamics adequately. Compared to the traditional DPLS Soft Sensor, the proposed RO-DPLS approach not only reduces model size and improves prediction but also provides multiple-step-ahead prediction. The performance of the proposed RO-DPLS is demonstrated using both a simulated single-vessel digester and an industrial Kamyr digester.
Olivier Bachelier - One of the best experts on this subject based on the ideXlab platform.
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Modeling and Simulation of Soft Sensor Design for Real-time Speed and Position Estimation of PMSM
ISA Transactions, 2015Co-Authors: Ines Omrane, Erik Etien, Wissam Dib, Olivier BachelierAbstract:This paper deals with the design of a speed Soft Sensor for permanent magnet synchronous motor. At high speed, model-based Soft Sensor is used and it gives excellent results. However, it fails to deliver satisfactory performance at zero or very low speed. High-frequency Soft Sensor is used at low speed. We suggest to use a model-based Soft Sensor together with the high-frequency Soft Sensor to overcome the limitations of the first one at low speed range .
Zhiqiang Ge - One of the best experts on this subject based on the ideXlab platform.
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co training partial least squares model for semi supervised Soft Sensor development
Chemometrics and Intelligent Laboratory Systems, 2015Co-Authors: Xiaofeng Yuan, Zhiqiang GeAbstract:Abstract Typically, the easy-to-measure variables are used to predict the hard-to-measure ones in Soft Sensor modeling. In practice, however, the easy-to-measure variables are redundant while the other ones are quite rare, which are often obtained from offline lab analyses. In this paper, the semi-supervised learning method is introduced for Soft Sensor modeling. Particularly, the co-training strategy is combined with the conventionally used partial least squares model (PLS). A co-training styled algorithm called co-training PLS is proposed for the development of a semi-supervised Soft Sensor. By splitting the whole process variables into two different parts, two diverse PLS regression models can be developed. Through an iterative learning procedure, the final new labeled data sets can be determined, based on which two new regressors are constructed for Soft sensing. Two examples are provided for performance evaluation of the proposed method, with detailed comparative studies to the traditional PLS and co-training k NN model based Soft Sensors.
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Soft Sensor model development in multiphase multimode processes based on gaussian mixture regression
Chemometrics and Intelligent Laboratory Systems, 2014Co-Authors: Xiaofeng Yuan, Zhiqiang Ge, Zhihuan SongAbstract:Abstract For complex industrial plants with multiphase/multimode data characteristic, Gaussian mixture model (GMM) has been used for Soft Sensor modeling. However, almost all GMM-based Soft Sensor modeling methods only employ GMM for identification of different operating modes, which means additional regression algorithms like PLS should be incorporated for quality prediction in different localized modes. In this paper, the Gaussian mixture regression (GMR) model is introduced for multiphase/multimode Soft Sensor modeling. In GMR, operating mode identification and variable regression are integrated into one model; thus, there is no need to switch prediction models when the operating mode changes from one to another. To improve the GMR model fitting performance, a heuristic algorithm is adopted for parameter initialization and component number optimization. Feasibility and efficiency of GMR based Soft Sensor are validated through a numerical example and two benchmark processes.
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nonlinear semisupervised principal component regression for Soft Sensor modeling and its mixture form
Journal of Chemometrics, 2014Co-Authors: Zhiqiang Ge, Biao Huang, Zhihuan SongAbstract:Compared with daily recorded process variables that can be easily obtained through the distributed control system, acquirements of key quality variables are much more difficult. As a result, for Soft Sensor development, we may only have a small number of output data samples and have much more input data samples. In this case, it is important to incorporate more input data samples to improve the modeling performance of the Soft Sensor. On the basis of the semisupervised modeling method, this paper aims to extend the linear semisupervised Soft Sensor to the nonlinear one, with incorporation of the kernel learning algorithm. Under the probabilistic modeling framework, a mixture form of the nonlinear semisupervised Soft Sensor is developed in the present work. To evaluate the performance of the developed nonlinear semisupervised Soft Sensor, an industrial case study is provided. Copyright © 2014 John Wiley & Sons, Ltd.
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nonlinear Soft Sensor development based on relevance vector machine
Industrial & Engineering Chemistry Research, 2010Co-Authors: Zhiqiang Ge, Zhihuan SongAbstract:This paper proposes an effective nonlinear Soft Sensor based on relevance vector machine (RVM), which was originally proposed in the machine learning area. Compared to the widely used support vecto...
Hector J Galicia - One of the best experts on this subject based on the ideXlab platform.
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Adaptive Outlier Detection and Classification for Online Soft Sensor Update
IFAC Proceedings Volumes, 2016Co-Authors: Hector J Galicia, Q. Peter He, Jin WangAbstract:Abstract Data-driven Soft Sensors that predict the primary variables of a process by using the secondary measurements have drawn increased research interests recently. They are easy to develop and only require a good historical data set. Among them, the partial least squares (PLS) based Soft Sensor is the most commonly used approach for industrial applications. As industrial processes often experience time-varying changes, it is desirable to update the Soft Sensor model with the new process data once the Soft Sensor is implemented online. Because the PLS algorithms are sensitive to outliers in the dataset, outlier detection and handling plays a critical role in the development of the PLS based Soft Sensors. In this work, we develop a multivariate approach for online outlier detection. In addition, to differentiate outliers caused by erroneous readings from those caused by process changes, we propose a Bayesian supervisory approach to analyze and classify the detected outliers. Finally, to address time-varying nature of industrial processes, we proposed a simple yet effective scheme to update the detection threshold. Both simulated and industrial case studies of the Kamyr digesters are used to demonstrate the effectiveness of the proposed approaches.
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a reduced order Soft Sensor approach and its application to a continuous digester
Journal of Process Control, 2011Co-Authors: Hector J Galicia, Peter Q He, Jin WangAbstract:Abstract In many industrial processes, the primary product variable(s) are not measured online but are required for feedback control. To address this challenge, there has been increased interest toward developing data-driven Soft Sensors using secondary measurements based on multivariate regression techniques. Among different data-driven approaches, the dynamic partial least squares (DPLS) Soft Sensor approach has been applied to several industrial processes. However, despite its successful applications, there is a lack of theoretical understanding on the properties of the DPLS Soft Sensor. Specifically, whether it can adequately capture process dynamics and whether it can provide unbiased estimate under closed-loop operation have not been examined rigorously. In this work, we provide a theoretical analysis to answer these questions. In addition, we propose a reduced-order DPLS (RO-DPLS) Soft Sensor approach to address the limitation of the traditional DPLS Soft Sensor when applied to model processes with large transport delay, i.e. , large number of lagged variables are required to be include in the regressor matrix in order to capture process dynamics adequately. Compared to the traditional DPLS Soft Sensor, the proposed RO-DPLS approach not only reduces model size and improves prediction but also provides multiple-step-ahead prediction. The performance of the proposed RO-DPLS is demonstrated using both a simulated single-vessel digester and an industrial Kamyr digester.
Zhihuan Song - One of the best experts on this subject based on the ideXlab platform.
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Soft Sensor model development in multiphase multimode processes based on gaussian mixture regression
Chemometrics and Intelligent Laboratory Systems, 2014Co-Authors: Xiaofeng Yuan, Zhiqiang Ge, Zhihuan SongAbstract:Abstract For complex industrial plants with multiphase/multimode data characteristic, Gaussian mixture model (GMM) has been used for Soft Sensor modeling. However, almost all GMM-based Soft Sensor modeling methods only employ GMM for identification of different operating modes, which means additional regression algorithms like PLS should be incorporated for quality prediction in different localized modes. In this paper, the Gaussian mixture regression (GMR) model is introduced for multiphase/multimode Soft Sensor modeling. In GMR, operating mode identification and variable regression are integrated into one model; thus, there is no need to switch prediction models when the operating mode changes from one to another. To improve the GMR model fitting performance, a heuristic algorithm is adopted for parameter initialization and component number optimization. Feasibility and efficiency of GMR based Soft Sensor are validated through a numerical example and two benchmark processes.
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nonlinear semisupervised principal component regression for Soft Sensor modeling and its mixture form
Journal of Chemometrics, 2014Co-Authors: Zhiqiang Ge, Biao Huang, Zhihuan SongAbstract:Compared with daily recorded process variables that can be easily obtained through the distributed control system, acquirements of key quality variables are much more difficult. As a result, for Soft Sensor development, we may only have a small number of output data samples and have much more input data samples. In this case, it is important to incorporate more input data samples to improve the modeling performance of the Soft Sensor. On the basis of the semisupervised modeling method, this paper aims to extend the linear semisupervised Soft Sensor to the nonlinear one, with incorporation of the kernel learning algorithm. Under the probabilistic modeling framework, a mixture form of the nonlinear semisupervised Soft Sensor is developed in the present work. To evaluate the performance of the developed nonlinear semisupervised Soft Sensor, an industrial case study is provided. Copyright © 2014 John Wiley & Sons, Ltd.
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nonlinear Soft Sensor development based on relevance vector machine
Industrial & Engineering Chemistry Research, 2010Co-Authors: Zhiqiang Ge, Zhihuan SongAbstract:This paper proposes an effective nonlinear Soft Sensor based on relevance vector machine (RVM), which was originally proposed in the machine learning area. Compared to the widely used support vecto...
