Results 251 to 260 of about 15,402,643 (297)

Multivariate statistical process monitoring based on principal discriminative component analysis

Journal of the Franklin Institute, 2021
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Shanzhi Li, Yang Chen, Chudong Tong
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A model updating approach of multivariate statistical process monitoring

2011 IEEE International Conference on Information and Automation, 2011
Multivariate statistical process control based on conventional principal component analysis (PCA) has been used widely in practice. The slow and normal changes in the processes often lead to false alarm since the conventional PCA algorithm is static. In this paper, we proposed a model updating approach of multivariate statistical process monitoring. By
null Bo He, null Xianhui Yang
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Generalized contribution plots in multivariate statistical process monitoring

Chemometrics and Intelligent Laboratory Systems, 2000
Abstract This paper discusses contribution plots for both the D -statistic and the Q -statistic in multivariate statistical process control of batch processes. Contributions of process variables to the D -statistic are generalized to any type of latent variable model with or without orthogonality constraints.
Westerhuis, J.A., Gurden, S.P., Smilde, A.K.   +2 more
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Application of Multivariate Statistical Process Monitoring to Lyophilization Process

2015
This chapter illustrates the concept of multivariate statistical process monitoring (MSPM) with data from a biopharmaceutical drug product lyophilization process. It provides the practitioner with steps needed for pretreatment of the data, modeling, and testing phases of the approach.
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Detectability study for statistical monitoring of multivariate dynamic processes

IIE Transactions, 2009
Fault detection and diagnosis for dynamic processes is an intensively investigated area. However, the problem of determining whether or not system faults can be successfully detected based on the output measurements for a given dynamic process remains an open research topic.
Nan Chen, Shiyu Zhou
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A Hierarchical Statistical Process Monitoring Strategy for Multivariable Multi-rate Industrial Processes

2009 WRI World Congress on Computer Science and Information Engineering, 2009
A hierarchical statistical process monitoring strategy is proposed for the industrial processes with multivariable multi-rate sampled measurements. By making full use of multi-rate measurements, two-level models are adopted where the sub-PCA models are built on high-rate measurements to ensure timely abnormality detection and the super Multi-block PCA ...
Jianhua Lu, Ningyun Lu
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A brief survey of different statistics for detecting multiplicative faults in multivariate statistical process monitoring

2016 IEEE 55th Conference on Decision and Control (CDC), 2016
The recent explosion in different statistics for fault detection has meant that the practitioner is faced with the unenviable job of determining which to use in a given situation. Thus, this paper seeks to investigate the different test statistics that can be applied to detect multiplicative faults for multivariate Gaussian-distributed processes in ...
Kai Zhang 0015, Yuri A. W. Shardt, Zhiwen Chen 0001, Steven X. Ding, Kaixiang Peng   +4 more
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Fault diagnosis in multivariate statistical process monitoring

2021
The application of multivariate statistical process monitoring (MSPM) methods has gained considerable momentum over the last couple of decades, especially in the processing industry for achieving higher throughput at sustainable rates, reducing safety related events and minimizing potential environmental impacts.
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Process monitoring and fault detection based on multivariate statistical projection analysis

2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), 2005
Multivariate statistical process control (MSPC) has been applied to performance monitoring for chemical process. However, conventional methods of MSPC are based on the premise that the extracted latent variables must be subjected to normal distribution, which often can't be satisfied. In this paper, a new method based on independent component analysis (
Guo-jin Chen, Jun Liang, Ji-Xin Qian
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Multivariate Statistical Process Monitoring Scheme with PLS and SVDD

2013
In order to adaptably monitor product qualities during real industrial process, a new multivariate statistical process monitoring scheme combining projection to latent spaces (PLS) and Support Vector Domain Description (SVDD) is proposed. PLS can establish the monitoring space, which maximizes the correlation between process variables and quality ...
Jia Liu, Yan-guang Sun
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