The Experts below are selected from a list of 15258 Experts worldwide ranked by ideXlab platform

Jie Dong - One of the best experts on this subject based on the ideXlab platform.

  • hierarchical monitoring and root cause diagnosis framework for key performance indicator related multiple faults in process industries
    IEEE Transactions on Industrial Informatics, 2019
    Co-Authors: Jie Dong, Kaixiang Peng, Chuanfang Zhang
    Abstract:

    In actual production processes, the occurrence probability of multiple faults is much higher than that of a single fault, which will affect the process industry operating performance and final products quality. This paper is concerned with industrial practices and theoretical approaches for detection and location of key performance indicator (KPI) related multiple faults in process industries. First, a new KPI-related multiple fault monitoring scheme is addressed from the subprocess level based on the developed correlation-based Canonical Variable analysis model. Then, Bayesian fusion is implemented to form the final monitoring decisions from the plantwide level. After that, a tensor subspace analysis-based discriminant analysis method is proposed for locating the root causes, which will help field engineers to take correction actions and recover the process operations. Finally, the application to a typical industry process, i.e., hot strip mill process, is given to demonstrate the performance and effectiveness of the proposed methods with real industrial data.

  • a novel data based quality related fault diagnosis scheme for fault detection and root cause diagnosis with application to hot strip mill process
    Control Engineering Practice, 2017
    Co-Authors: Jie Dong, Kaixiang Peng, Kai Zhang
    Abstract:

    Abstract In this paper, a new technology or solution of quality-related fault diagnosis is provided for hot strip mill process (HSMP). Different from traditional data-based fault diagnosis methods, the alternative approach is focused more on root cause diagnosis. The new scheme addresses the quality-related fault detection with the developed modified Canonical Variable analysis (MCVA) model, then the advantage of original generalized reconstruction based contribution (GRBC) is followed to identify the faulty Variables. Meanwhile, a new transfer entropy (TE)-based causality analysis method is proposed for root cause diagnosis of quality-related faults. Finally, the whole proposed framework is practiced with real HSMP data, and the results demonstrate the usage and effectiveness of these approaches.

Chuanfang Zhang - One of the best experts on this subject based on the ideXlab platform.

  • hierarchical monitoring and root cause diagnosis framework for key performance indicator related multiple faults in process industries
    IEEE Transactions on Industrial Informatics, 2019
    Co-Authors: Jie Dong, Kaixiang Peng, Chuanfang Zhang
    Abstract:

    In actual production processes, the occurrence probability of multiple faults is much higher than that of a single fault, which will affect the process industry operating performance and final products quality. This paper is concerned with industrial practices and theoretical approaches for detection and location of key performance indicator (KPI) related multiple faults in process industries. First, a new KPI-related multiple fault monitoring scheme is addressed from the subprocess level based on the developed correlation-based Canonical Variable analysis model. Then, Bayesian fusion is implemented to form the final monitoring decisions from the plantwide level. After that, a tensor subspace analysis-based discriminant analysis method is proposed for locating the root causes, which will help field engineers to take correction actions and recover the process operations. Finally, the application to a typical industry process, i.e., hot strip mill process, is given to demonstrate the performance and effectiveness of the proposed methods with real industrial data.

Kaixiang Peng - One of the best experts on this subject based on the ideXlab platform.

  • hierarchical monitoring and root cause diagnosis framework for key performance indicator related multiple faults in process industries
    IEEE Transactions on Industrial Informatics, 2019
    Co-Authors: Jie Dong, Kaixiang Peng, Chuanfang Zhang
    Abstract:

    In actual production processes, the occurrence probability of multiple faults is much higher than that of a single fault, which will affect the process industry operating performance and final products quality. This paper is concerned with industrial practices and theoretical approaches for detection and location of key performance indicator (KPI) related multiple faults in process industries. First, a new KPI-related multiple fault monitoring scheme is addressed from the subprocess level based on the developed correlation-based Canonical Variable analysis model. Then, Bayesian fusion is implemented to form the final monitoring decisions from the plantwide level. After that, a tensor subspace analysis-based discriminant analysis method is proposed for locating the root causes, which will help field engineers to take correction actions and recover the process operations. Finally, the application to a typical industry process, i.e., hot strip mill process, is given to demonstrate the performance and effectiveness of the proposed methods with real industrial data.

  • a novel data based quality related fault diagnosis scheme for fault detection and root cause diagnosis with application to hot strip mill process
    Control Engineering Practice, 2017
    Co-Authors: Jie Dong, Kaixiang Peng, Kai Zhang
    Abstract:

    Abstract In this paper, a new technology or solution of quality-related fault diagnosis is provided for hot strip mill process (HSMP). Different from traditional data-based fault diagnosis methods, the alternative approach is focused more on root cause diagnosis. The new scheme addresses the quality-related fault detection with the developed modified Canonical Variable analysis (MCVA) model, then the advantage of original generalized reconstruction based contribution (GRBC) is followed to identify the faulty Variables. Meanwhile, a new transfer entropy (TE)-based causality analysis method is proposed for root cause diagnosis of quality-related faults. Finally, the whole proposed framework is practiced with real HSMP data, and the results demonstrate the usage and effectiveness of these approaches.

Xiaoling Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Variable sub region Canonical variate analysis for dynamic process monitoring
    IEEE Access, 2020
    Co-Authors: Yuping Cao, Xiaogang Deng, Xiaoling Zhang
    Abstract:

    When all process Variables are used to establish one data-based model, some Variables have little contributions to the model. When these Variables are grouped into a small local Variable set to develop a data-based model, their contributions will become large. It is called local Variable characteristic. Standard Canonical variate analysis (CVA) based process monitoring method doesn’t consider local process Variable characteristic. To solve this problem, a Variable sub-region based Canonical variate analysis (V-CVA) is proposed for dynamic process monitoring by enhancing the information of local Variables. The proposed method combines Variable sub-region division and Bayesian fusion. First, standard CVA is performed by using all process Variables. Then, K-means clustering is adopted to divide process Variables into sub-regions based on the mutual information of process Variables and Canonical Variables. For each Variable sub-region, CVA is conducted to compute local process monitoring statistics. Finally, local statistics are fused to build ensemble statistics based on the idea of Bayesian inference. Compared with the standard CVA method, the proposed V-CVA method can emphasize the information of local Variables, and has better monitoring performance in the Canonical Variable feature subspace and the prediction residual subspace. Two examples demonstrate the effectiveness of the proposed method.

Yuping Cao - One of the best experts on this subject based on the ideXlab platform.

  • Variable sub region Canonical variate analysis for dynamic process monitoring
    IEEE Access, 2020
    Co-Authors: Yuping Cao, Xiaogang Deng, Xiaoling Zhang
    Abstract:

    When all process Variables are used to establish one data-based model, some Variables have little contributions to the model. When these Variables are grouped into a small local Variable set to develop a data-based model, their contributions will become large. It is called local Variable characteristic. Standard Canonical variate analysis (CVA) based process monitoring method doesn’t consider local process Variable characteristic. To solve this problem, a Variable sub-region based Canonical variate analysis (V-CVA) is proposed for dynamic process monitoring by enhancing the information of local Variables. The proposed method combines Variable sub-region division and Bayesian fusion. First, standard CVA is performed by using all process Variables. Then, K-means clustering is adopted to divide process Variables into sub-regions based on the mutual information of process Variables and Canonical Variables. For each Variable sub-region, CVA is conducted to compute local process monitoring statistics. Finally, local statistics are fused to build ensemble statistics based on the idea of Bayesian inference. Compared with the standard CVA method, the proposed V-CVA method can emphasize the information of local Variables, and has better monitoring performance in the Canonical Variable feature subspace and the prediction residual subspace. Two examples demonstrate the effectiveness of the proposed method.

  • Dynamic process fault prediction using Canonical Variable trend analysis
    2017 Chinese Automation Congress (CAC), 2017
    Co-Authors: Xiaogang Deng, Yuping Cao, Xuemin Tian
    Abstract:

    Fault prediction technology is important to avoid serious process failure. This paper is concerned with the fault prediction of dynamic industrial process with incipient faults and proposes a Canonical Variable trend analysis (CVTA) based fault prediction method. In the proposed method, Canonical variate analysis (CVA) algorithm is firstly applied to analyze the process dynamics and extract the uncorrelated latent features, called Canonical Variables. Furthermore, support vector machine is adopted to model the relationship between the historical and future values of the Canonical Variables, which leads to the time series prediction model for the Canonical Variables. Based on the predicted Canonical Variables, an overall monitoring statistic is used to forecast the change of the process status. Simulations on a continuous stirred tank reactor (CSTR) system demonstrate that the proposed method can indicate the trend of the incipient faults effectively.