The Experts below are selected from a list of 362115 Experts worldwide ranked by ideXlab platform
Zhihuan Song - One of the best experts on this subject based on the ideXlab platform.
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bagging support vector data description model for batch process Monitoring
Journal of Process Control, 2013Co-Authors: Zhihuan SongAbstract:Abstract To improve the Monitoring Performance of the support vector data description model (SVDD), an ensemble form of SVDD is developed, which is termed as bagging SVDD in this paper. While different kinds of ensemble learning approaches have been developed in the past years, bagging is probably the most traditional and simplest one. By randomly selecting subsets from the original dataset, bagging constructs an individual SVDD model for each of these subsets. For practical utilization, the results of different individual SVDD models are ensembled/combined together. In this paper, two kinds of combination strategies are proposed, named as voting-based strategy and Bayesian-based strategy. Compared to a single SVDD model, the Monitoring Performance can be improved by the bagging SVDD method in most cases. The feasibility and effectiveness of the proposed method are demonstrated by an industrial semiconductor etch process.
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Performance driven ensemble learning ica model for improved non gaussian process Monitoring
Chemometrics and Intelligent Laboratory Systems, 2013Co-Authors: Zhiqiang Ge, Zhihuan SongAbstract:Abstract Although successful application studies of independent component analysis (ICA) have been reported for non-Gaussian process Monitoring, there are several drawbacks of this method, which make it cumbersome for practical utilization. First, due to the random initialization of the ICA algorithm, the Monitoring Performance of the ICA-based method is unstable, which may confuse the result. Second, the number selection of retained independent components (ICs) is still an open problem. Third, how to measure the importance of each IC for process Monitoring purpose is also a difficult task so far. To address these issues, this paper intends to improve the ICA statistical Monitoring method by incorporating the ensemble learning approach and the Bayesian inference strategy. Besides, a new Performance-driven approach for IC number selection is also proposed. As a result, the stability of the non-Gaussian process Monitoring result is greatly improved. Meanwhile, the Monitoring Performance is also boosted up, which is illustrated through the Tennessee Eastman (TE) benchmark case study.
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batch process Monitoring based on support vector data description method
Journal of Process Control, 2011Co-Authors: Furong Gao, Zhihuan SongAbstract:Abstract Process Monitoring can be considered as a one-class classification problem, the aim of which is to differentiate the normal data samples from the faulty ones. This paper introduces an efficient one-class classification method for batch process Monitoring, which is called support vector data description (SVDD). Different from the traditional data description method such as principal component analysis (PCA) and partial least squares (PLS), SVDD has no Gaussian assumption of the process data, and is also effective for nonlinear process modeling. Furthermore, SVDD only incorporates a quadratic optimization step, which makes it easy for practical implementation. Based on the basic SVDD batch process Monitoring approach, the method is further extended to multiphase and multimode batch processes. Two case studies are provided to evaluate the Monitoring Performance of the proposed methods.
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Maximum-likelihood mixture factor analysis model and its application for process Monitoring
Chemometrics and Intelligent Laboratory Systems, 2010Co-Authors: Zhihuan SongAbstract:Abstract In the present paper, a mixture form of the factor analysis model is developed under the maximum-likelihood framework. In this new model structure, different noise levels of process variables have been considered. Afterward, the developed mixture factor analysis model is utilized for process Monitoring. To enhance the Monitoring Performance, a soft combination strategy is then proposed to integrate different local Monitoring results into a single Monitoring chart, which is based on the Bayesian inference method. To test the modeling and Monitoring Performance of the proposed mixture factor analysis method, a numerical example and the Tennessee Eastman (TE) benchmark case studies are provided.
Khalid Lafdi - One of the best experts on this subject based on the ideXlab platform.
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Real-time strain Monitoring Performance of flexible Nylon/Ag conductive fiber
Sensors and Actuators A: Physical, 2019Co-Authors: Yumna Qureshi, Mostapha Tarfaoui, Khalid LafdiAbstract:Smart textiles have generated significant importance because of the advent of portable devices and easy computing, however, they did not replace the conventional electronics on the whole however, this development is now advanced to the fabrication of wearable technologies. The aim of this research paper was to develop a flexible microscale conductive fiber for real-time strain Monitoring applications. This conductive fiber was developed by depositing conductive silver (Ag) nanoparticles on the surface of Nylon-6 polymer yarn by electroless plating process to achieve smallest uniform coating film over each filament of the Nylon yarn without jeopardizing the integrity of each material. The sensitivity of this Nylon/Ag conductive fiber was calculated experimentally and gauge factor was found to be in the range of 21-25 which showed that it had high sensitivity to the applied strain. Then, Nylon/Ag conductive fiber was tested up to fracture under tensile loading and a good agreement between mechanical and electrical response was observed with reproducibility of the results. The results demonstrated the way to design a cost-effective microscale smart textile for strain Monitoring. This Nylon/Ag conductive fiber can then be used in a wide range of high strain applications such as in-situ structural health Monitoring or for medical Monitoring because of their high sensitivity, flexibility, and stability.
Yumna Qureshi - One of the best experts on this subject based on the ideXlab platform.
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Real-time strain Monitoring Performance of flexible Nylon/Ag conductive fiber
Sensors and Actuators A: Physical, 2019Co-Authors: Yumna Qureshi, Mostapha Tarfaoui, Khalid LafdiAbstract:Smart textiles have generated significant importance because of the advent of portable devices and easy computing, however, they did not replace the conventional electronics on the whole however, this development is now advanced to the fabrication of wearable technologies. The aim of this research paper was to develop a flexible microscale conductive fiber for real-time strain Monitoring applications. This conductive fiber was developed by depositing conductive silver (Ag) nanoparticles on the surface of Nylon-6 polymer yarn by electroless plating process to achieve smallest uniform coating film over each filament of the Nylon yarn without jeopardizing the integrity of each material. The sensitivity of this Nylon/Ag conductive fiber was calculated experimentally and gauge factor was found to be in the range of 21-25 which showed that it had high sensitivity to the applied strain. Then, Nylon/Ag conductive fiber was tested up to fracture under tensile loading and a good agreement between mechanical and electrical response was observed with reproducibility of the results. The results demonstrated the way to design a cost-effective microscale smart textile for strain Monitoring. This Nylon/Ag conductive fiber can then be used in a wide range of high strain applications such as in-situ structural health Monitoring or for medical Monitoring because of their high sensitivity, flexibility, and stability.
Qingchao Jiang - One of the best experts on this subject based on the ideXlab platform.
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review and perspectives of data driven distributed Monitoring for industrial plant wide processes
Industrial & Engineering Chemistry Research, 2019Co-Authors: Qingchao Jiang, Xuefeng Yan, Biao HuangAbstract:Process Monitoring is crucial for maintaining favorable operating conditions and has received considerable attention in previous decades. Currently, a plant-wide process generally consists of multiple operational units and a large number of measured variables. The correlation among the variables and units is complex and results in the imperative but challenging Monitoring of such plant-wide processes. With the rapid advancement of industrial sensing techniques, process data with meaningful process information are collected. Data-driven multivariate statistical plant-wide process Monitoring (DMSPPM) has become popular. The key idea of DMSPPM is first decomposing a plant-wide process into multiple subprocesses and then establishing a data-driven model for Monitoring the process, in which process variable decomposition is important for guaranteeing the Monitoring Performance. In the current review, we first introduce the basics of multivariate statistical process Monitoring and highlight the necessity of des...
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distributed Monitoring for large scale processes based on multivariate statistical analysis and bayesian method
Journal of Process Control, 2016Co-Authors: Qingchao Jiang, Biao HuangAbstract:Abstract Large-scale plant-wide processes have become more common and Monitoring of such processes is imperative. This work focuses on establishing a distributed Monitoring scheme incorporating multivariate statistical analysis and Bayesian method for large-scale plant-wide processes. First, the necessity of distributed Monitoring is demonstrated by theoretical analysis on the impact of process decomposition on multivariate statistical process Monitoring Performance. Second, a stochastic optimization algorithm-based Performance-driven process decomposition method is proposed which aims to achieve the best possible Monitoring Performance from process decomposition aspect. Based on the obtained sub-blocks, local monitors are established to characterize local process behaviors, and then a Bayesian fault diagnosis system is established to identify the underlying process status of the entire process. The proposed distributed Monitoring scheme is applied on a numerical example and the Tennessee Eastman benchmark process. Comparison results to some state-of-the-art methods indicate the efficiency and feasibility.
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Performance driven distributed pca process Monitoring based on fault relevant variable selection and bayesian inference
IEEE Transactions on Industrial Electronics, 2016Co-Authors: Qingchao Jiang, Xuefeng Yan, Biao HuangAbstract:Multivariate statistical process Monitoring involves dimension reduction and latent feature extraction in large-scale processes and typically incorporates all measured variables. However, involving variables without beneficial information may degrade Monitoring Performance. This study analyzes the effect of variable selection on principal component analysis (PCA) Monitoring Performance. Then, it proposes a fault-relevant variable selection and Bayesian inference-based distributed method for efficient fault detection and isolation. First, the optimal subset of variables is identified for each fault using an optimization algorithm. Second, a sub-PCA model is established in each subset. Finally, the Monitoring results of all of the subsets are combined through Bayesian inference. The proposed method reduces redundancy and complexity, explores numerous local behaviors, and provides accurate description of faults, thus improving Monitoring Performance significantly. Case studies on a numerical example, the Tennessee Eastman benchmark process, and an industrial-scale plant demonstrate the efficiency.
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independent component analysis based non gaussian process Monitoring with preselecting optimal components and support vector data description
International Journal of Production Research, 2014Co-Authors: Qingchao Jiang, Xuefeng Yan, Meijin GuoAbstract:Independent component analysis (ICA)-based process Monitoring methods have rapidly progressed, but independent components (ICs) selection remains an open question. Subjective ICs selection would lead to useful information dispersion and affect the ICA Monitoring Performance. A novel ICA-based method integrated with preselecting optimal components and support vector machine data description (SVDD) technique is proposed to improve the non-Gaussian process Monitoring Performance. The proposed method first concentrates the informative ICs into one subspace for each fault and then the SVDD is employed to examine the variations in all subspaces. Case studies on a simulated process and Tennessee Eastman benchmark process demonstrate the effectiveness of the proposed scheme. The Monitoring Performances are significantly improved compared with the conventional ICA method.
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fault detection and diagnosis in chemical processes using sensitive principal component analysis
Industrial & Engineering Chemistry Research, 2013Co-Authors: Qingchao Jiang, Weixiang ZhaoAbstract:Sensitive principal component analysis (SPCA) is proposed to improve the principal component analysis (PCA) based chemical process Monitoring Performance, by solving the information loss problem and reducing nondetection rates of the T2 statistic. Generally, principal components (PCs) selection in the PCA-based process Monitoring is subjective, which can lead to information loss and poor Monitoring Performance. The SPCA method is to subsequently build a conventional PCA model based on normal samples, index PCs which reflect the dominant variation of abnormal observations, and use these sensitive PCs (SPCs) to monitor the process. Moreover, a novel fault diagnosis approach based on SPCA is also proposed due to SPCs’ ability to represent the main characteristic of the fault. The case studies on the Tennessee Eastman process demonstrate the effect of SPCA on online Monitoring, showing its Performance is significantly better than that of the classical PCA methods.
Mostapha Tarfaoui - One of the best experts on this subject based on the ideXlab platform.
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Real-time strain Monitoring Performance of flexible Nylon/Ag conductive fiber
Sensors and Actuators A: Physical, 2019Co-Authors: Yumna Qureshi, Mostapha Tarfaoui, Khalid LafdiAbstract:Smart textiles have generated significant importance because of the advent of portable devices and easy computing, however, they did not replace the conventional electronics on the whole however, this development is now advanced to the fabrication of wearable technologies. The aim of this research paper was to develop a flexible microscale conductive fiber for real-time strain Monitoring applications. This conductive fiber was developed by depositing conductive silver (Ag) nanoparticles on the surface of Nylon-6 polymer yarn by electroless plating process to achieve smallest uniform coating film over each filament of the Nylon yarn without jeopardizing the integrity of each material. The sensitivity of this Nylon/Ag conductive fiber was calculated experimentally and gauge factor was found to be in the range of 21-25 which showed that it had high sensitivity to the applied strain. Then, Nylon/Ag conductive fiber was tested up to fracture under tensile loading and a good agreement between mechanical and electrical response was observed with reproducibility of the results. The results demonstrated the way to design a cost-effective microscale smart textile for strain Monitoring. This Nylon/Ag conductive fiber can then be used in a wide range of high strain applications such as in-situ structural health Monitoring or for medical Monitoring because of their high sensitivity, flexibility, and stability.