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3rd International Conference on Applied Mechanics, Materials and Manufacturing (ICAMMM 2013)

作者： Chen, Ru Qing Jiaxing Univ Coll Mech & Elect Engn Jiaxing 314001 Zhejiang Peoples R China

ISBN: (纸本)9783037858882

In modern industrial processes, effective performance monitoring and quality prediction are the key to ensure plant safety and enhance product quality. The research significance and background of process monitoring and fault diagnosis technologies are described and the current advances in data-based process monitoring methods are summed up in this paper. Then the multivariate statistical process control (MSPC) methods for process with single constraint, especially for single non-Gaussian process or nonlinear process are elaborated. As real industrial process data often show strong non-Gaussian and dynamic behaviors, study on monitoring technologies for dynamic non-Gaussian process is of great importance. Finally, some challenges such as non-Gaussian and dynamic process, fault detection and diagnosis as well as new MSPC methods are indicated.

关键词： Data-driven technique process monitoring and fault diagnosis multivariate statistical process control Dynamic and non-Gaussian process

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Nonlinear multivariate statistical process Monitoring of a Water Treatment Plant

Nonlinear Multivariate Statistical Process Monitoring of a W...

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5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)

作者： Mendaci, Khaled Ramdani, Messaoud Benzaraa, Toufik Univ Badji Mokhtar Annaba LASA Annaba 23000 Algeria

ISBN: (纸本)9781467358149;9781467358125

In this paper, a multivariate statistical process monitoring technique based on bottleneck auto-associative neural network is applied on a water treatment plant. First, the nonlinear principal component analysis (NLPCA) is carried out in order to identify and analyze the relationships among the correlated variables in the process by compressing multidimensional data set, extracting the original data from the principal components and then the squared prediction error is evaluated to find the erroneous data samples. So, the information obtained using this intelligent tool is used for diagnosis. The obtained results on realistic data demonstrate the effectiveness of the applied technique for monitoring water treatment plants.

关键词： process monitoring multivariate statistical process control Diagnosis Nonlinear Principal Component Analysis Water Treatment Plant Auto-Associative Neural Network

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Use of NLPCA for Sensors Fault Detection and Localization Applied at WTP

Use of NLPCA for Sensors Fault Detection and Localization Ap...

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World Congress on Computer and Information Technologies (WCCIT)

作者： Bouzenad, K. Ramdani, M. Zermi, N. Mendaci, K. Badji Mokhtar Univ Dept Elect LASA Annaba 23000 Algeria

ISBN: (纸本)9781479904624

Principal Components Analysis (PCA) has been intensively studied and is widely applied in industrial process monitoring. The main purpose of using PCA is the dimensionality reduction by extraction of the feature space that still contain the most information in the original data set. Despite its success in this field, the most important obstacle faced is the sensitivity to noise, also the fact that the majority of collected data from industrial processes are normally contaminated by noise makes it unreliable in some cases. To overcome these limitations, several strategies have been used. One of these has been interested to combine the robustness theory with PCA method, such theory sonsists in robustifying the existing algorithms against noise or outliers. Fuzzy Robust Principal Components Analysis (FRPCA) is one of the result for such combination that acheive better result compared with the classical method. In this work the RFPCA method is used and compared with the classical one to monitoring a biological nitrogen removal process. The obtained results demonstrate the performances superiority of this method compared with the conventional one.

关键词： process monitoring multivariate statistical process control fault diagnosis NLPCA Water Treatment Plant Sensor Validity Index

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Improving the robustness of particle size analysis by multivariate statistical process control

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PARTICLE & PARTICLE SYSTEMS CHARACTERIZATION 2007年第3期24卷 173-183页

作者： Mattila, Marko Saloheimo, Kari Koskinen, Kari Aalto Univ Lab Informat & Comp Syst Automat Helsinki 02015 TKK Finland Outotec Oyj Espoo 02200 Finland

The robustness of online particle size analysis in wet processes is improved by applying data based modeling methods to the control of the sample preparation and measurement sequence of the particle size analyzer. The aim is to find a more accurate and reliable method of determining the end of the particle size integration period using multivariate statistical process control (MSPC). The studied approach is tested on analyzers installed at two mineral processing plant sites and validated using two validation tests. Research shows that the proposed method works with two very different slurry types. The main advantage of the adapted approach is that there are no adjustable parameters that have to be set by the user.

关键词： laser diffraction multivariate statistical process control particle size distribution principal component analysis robustness

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Variable sampling interval hotelling's T2 charts with runs rules for switching between sampling interval lengths

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2012年第2期28卷 131-140页

作者： Mahadik, Shashibhushan B. DRK Coll Commerce Dept Stat Kolhapur 416002 Maharashtra India

The use of runs rules is proposed for switching between the sampling interval lengths of variable sampling interval Hotelling's T2 charts. The purpose of applying these rules is to reduce the frequency of the switches which causes inconvenience in the administration of the charts. The expressions for the performance measures for the charts with these rules are derived. The effects of different runs rules on the performances are evaluated through numerical comparisons. The runs rules substantially reduce the frequency of switches during the in-control period and during the out-of-control periods due to the small to moderate shifts in the process mean vector. They also fairly improve the statistical performances of the charts in detecting the small shifts and do not affect that in detecting the large shifts. However, some runs rules slightly worsen the statistical performances in detecting the moderate shifts. Copyright (c) 2011 John Wiley & Sons, Ltd.

关键词： adaptive control chart average time to signal average number of samples to signal average number of switches to signal multivariate statistical process control

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Local and global principal component analysis for process monitoring

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JOURNAL OF process control 2012年第7期22卷 1358-1373页

作者： Yu, Jianbo Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

In this paper, a novel data projection method, local and global principal component analysis (LGPCA) is proposed for process monitoring. LGPCA is a linear dimensionality reduction technique through preserving both of local and global information in the observation data. Beside preservation of the global variance information of Euclidean space that principal component analysis (PCA) does, LGPCA is characterized by capturing a good linear embedding that preserves local structure to find meaningful low-dimensional information hidden in the high-dimensional process data. LGPCA-based T-2 (D) and squared prediction error (Q) statistic control charts are developed for on-line process monitoring. The validity and effectiveness of LGPCA-based monitoring method are illustrated through simulation processes and Tennessee Eastman process (TEP). The experimental results demonstrate that the proposed method effectively captures meaningful information hidden in the observations and shows superior process monitoring performance compared to those regular monitoring methods. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： process monitoring multivariate statistical process control Manifold learning Principal component analysis

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Reconstruction-based multivariate contribution analysis for fault isolation: A branch and bound approach

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JOURNAL OF process control 2012年第7期22卷 1228-1236页

作者： He, Bo Yang, Xianhui Chen, Tao Zhang, Jie Univ Surrey Div Civil Chem & Environm Engn Guildford GU2 7XH Surrey England Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Newcastle Univ Sch Chem Engn & Adv Mat Newcastle Upon Tyne NE1 7RU Tyne & Wear England

Identification of faulty variables is an important component of multivariate statistical process monitoring (MSPM);it provides crucial information for further analysis of the root cause of the detected fault. The main challenge is the large number of combinations of process variables under consideration, usually resulting in a combinatorial optimization problem. This paper develops a generic reconstruction based multivariate contribution analysis (RBMCA) framework to identify the variables that are the most responsible for the fault. A branch and bound (BAB) algorithm is proposed to efficiently solve the combinatorial optimization problem. The formulation of the RBMCA does not depend on a specific model, which allows it lobe applicable to any MSPM model. We demonstrate the application of the RBMCA to a specific model: the mixture of probabilistic principal component analysis (PPCA mixture) model. Finally, we illustrate the effectiveness and computational efficiency of the proposed methodology through a numerical example and the benchmark simulation of the Tennessee Eastman process. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Branch and bound Combinatorial optimization Fault detection and isolation multivariate contribution analysis multivariate statistical process control

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A Variable-Selection-Based multivariate EWMA Chart for process Monitoring and Diagnosis

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JOURNAL OF QUALITY TECHNOLOGY 2012年第3期44卷 209-230页

作者： Jiang, Wei Wang, Kaibo Tsung, Fugee Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai 200052 Peoples R China Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Hong Kong Univ Sci & Technol Dept Ind Engn & Logist Management Hong Kong Hong Kong Peoples R China

Fault detection and root cause identification are both important tasks in multivariate statistical process control (MSPC) for improving process and product quality. Most traditional control charts, including Hotelling's T-2 chart and the multivariate Exponential Weighted Moving Average (MEWMA) chart, separate the two tasks into independent and successive procedures by signaling the existence of process faults followed by auxiliary methods to locate root causes. This paper proposes an integrated procedure, a Variable-Selection-based MEWMA (VS-MEWMA) chart, for multivariate process monitoring and fault diagnosis by utilizing dimensionality reduction techniques. The VS-MEWMA chart first locates potentially out-of-control variables via variable selection and then deploys such information in the monitoring statistics with the reduction in dimensionality providing increased sensitivity to out-of-control conditions. When a signal is given, the algorithm also identifies the suspected variables for further root cause diagnosis. Both numerical simulations and real examples are presented to illustrate the performance of the proposed chart, as well as design guidelines.

关键词： Average Run Length Fault Diagnosis Forward Selection MEWMA multivariate statistical process control

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A large-scale statistical process control approach for the monitoring of electronic devices assemblage

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COMPUTERS & CHEMICAL ENGINEERING 2012年 39卷 163-169页

作者： Reis, Marco S. Delgado, Pedro Univ Coimbra Dept Chem Engn CIEPQPF P-3030790 Coimbra Portugal

In this paper, we present a new procedure for monitoring the assembly process of electronic devices. Monitoring the status of this operation is a challenge, as the number of quality features under monitoring is very large (order of thousands) and the number of samples available quite low (order of dozens). We propose an efficient approach for the on-line and at-line monitoring of such a process, by addressing two, hierarchically related. problems: (i) detection of faulty units (printed circuits boards with abnormal deposits): (ii) given a faulty unit, find a candidate set of solder deposits responsible for the anomaly. Our methodology is based on a latent variable framework using PCA for effectively extracting the normal behavior of the process. Both the variability in the PCA plane and around it (residuals) are considered. We have tested the proposed approach with real industrial data, and the results achieved illustrate its good discrimination ability. (c) 2011 Elsevier Ltd. All rights reserved.

关键词： Printed circuits boards Principal components analysis multivariate statistical process control Receiver operating characteristic

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A Bayesian Approach for Interpreting Mean Shifts in multivariate Quality control

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TECHNOMETRICS 2012年第3期54卷 294-307页

作者： Tan, Matthias H. Y. Shi, Jianjun Georgia Inst Technol Coll Engn H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

multivariate quality characteristics are often monitored using a single statistic or a few statistics. However, it is difficult to determine the causes of an out-of-control signal based on a few summary statistics. Therefore, if a control chart for the mean detects a change in the mean, the quality engineer needs to determine which means shifted and the directions of the shifts to facilitate identification of root causes. We propose a Bayesian approach that gives a direct answer to this question. For each mean, an indicator variable that indicates whether the mean shifted upward, shifted downward, or remained unchanged is introduced. Prior distributions for the means and indicators capture prior knowledge about mean shifts and allow for asymmetry in upward and downward shifts. The mode of the posterior distribution of the vector of indicators or the mode of the marginal posterior distribution of each indicator gives the most likely scenario for each mean. Evaluation of the posterior probabilities of all possible values of the indicators is avoided by employing Gibbs sampling. This renders the computational cost more affordable for high-dimensional problems. This article has supplementary materials online.

关键词： Fault isolation Gibbs sampling Hierarchical Bayes Interpretation of out-of-control signal multivariate statistical process control Variable selection

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