scholarly journals Statistical Process Control and More about Multi-way Principal Component Analysis

2009 ◽  
Vol 27 (2) ◽  
pp. 1-9
Author(s):  
Choi, Hwanseok ◽  
김영일
Keyword(s):  
Principal Component Analysis ◽  
Process Control ◽  
Statistical Process Control ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Process

scholarly journals Fault Analysis Of Process System Using Multi Block Principal Component Analysis

REAKTOR ◽  
2017 ◽  
Vol 7 (02) ◽  
pp. 61
Author(s):  
S. B. Sasongko ◽  
K. A. Ibrahim ◽  
A. Ahmad
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Analysis ◽  
Multivariate Statistical ◽  
Statistical Process

This research looks into the issues of the quality improvement based on process control instead of product control using multivariate statistical process contro. A deterministic model of a proton exchange membrane fuel cell (PEM-FC) power plant was used as a case study to represent a multi variable or mukti equipment system. A three-step approach is proposed which  can be classified into fault detection, fault isolation, and faulr diagnosis. The fault detection and the isolation utilize the multivariate analysis and yhe contro chart method , which uses the series multi-block principal component analysis  of extended of PCA method. The series block principal component abalysis is solved using the non linear iteration partial least squares (NIPALS) algorithm. The SB-PCA can advangeouly incorporate the control chart, namely, T2 Hotelling control chart. In the fault diagnosis chart, the normalized variable method was successfully applied in this study with promising results. As a conclution, the result of this study demonstrated the potentials of multivariate statistical process control in solving fault detection and diagnosis problem for multi variable and multi equipment system.Keywords : statistical process control, principal component, fault analysis

Performance of Statistical Process Control Tools under Different Plant Operation Strategies

2008 ◽  
Vol 3 (1) ◽  
Author(s):  
Mauricio L. Maestri ◽  
Miryan C. Cassanello ◽  
Gabriel I. Horowitz
Keyword(s):  
Principal Component Analysis ◽  
Process Control ◽  
Statistical Process Control ◽  
Principal Component ◽  
Industrial Plant ◽  
Kernel Pca ◽  
Statistical Process ◽  
Plant Operation ◽  
Standard Principal Component Analysis ◽  
Standard Operation

The outputs of statistical process control (SPC) tools developed for fault detection are comparatively examined while applied to actual data collected in an industrial plant. The influence of added information gathered from the plant operation under different strategies is analyzed. Particularly, standard principal component analysis (PCA), kernel PCA and the Hotelling's T2 charts are inspected for a reported problem. The effect of training the tools either with an extended historic databank obtained under standard operation, or including also non-conventional conditions, is studied. The ability of the tools to provide a specific alarm and identify the responsible variable is examined by analyzing the contributions per variable to the SPE and the T2 statistics. In addition, the capacity of the tested tools to adapt to a new operation strategy is compared.

Research on Nonlinear Process Monitoring and Fault Diagnosis Based on Kernel Principal Component Analysis

2009 ◽  
Vol 413-414 ◽  
pp. 583-590 ◽  
Author(s):  
Fei He ◽  
Min Li ◽  
Jian Hong Yang ◽  
Jin Wu Xu
Keyword(s):  
Principal Component Analysis ◽  
Production Process ◽  
Statistical Process Control ◽  
Process Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Multivariate Statistical Process Control ◽  
Multivariate Statistical ◽  
Statistical Process

In order to monitor nonlinear production process effectively, multivariate statistical process control based on kernel principal component analysis is applied to process monitoring and diagnosis. Squared prediction error (SPE) statistic of the kernel principal component analysis (KPCA) model is used for process monitoring, and the fault causes of the production process could be tracked by the methods of data reconstruction and the optimal neighbor selection strategy. Simulation data and Tennessee Eastman process data are used for model validation, as a result the proposed method has better performance on abnormality detecting, compared with multivariate statistical process control based on linear principal component analysis. What is more, the causes of the faults are tracked effectively, thus the production process can be adjusted to prevent substandard products.

Standardization of CMM Fitting Algorithms and Development of Inspection Maps for Use in Statistical Process Control

2011 ◽  
Author(s):  
Neelakantan Mani ◽  
Jami J. Shah ◽  
Joseph K. Davidson
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Principal Component ◽  
Statistical Control ◽  
Statistical Process ◽  
Fitting Algorithm ◽  
Inspection Process ◽  
Feature Fitting ◽  
The Individual ◽  
Intelligent Methods

The choice of fitting algorithm in CMM metrology has often been based on mathematical convenience rather than the fundamental GD&T principles dictated by the ASME Y14.5 standard. Algorithms based on the least squares technique are mostly used for GD&T inspection and this wrong choice of fitting algorithm results in errors that are often overlooked and leads to deficiency in the inspection process. The efforts by organizations such as NIST and NPL and many other researchers to evaluate commercial CMM software were concerned with the mathematical correctness of the algorithms and developing efficient and intelligent methods to overcome the inherent difficulties associated with the mathematics of these algorithms. None of these works evaluate the ramifications of the choice of a particular fitting algorithm for a particular tolerance type. To illustrate the errors that can arise out of a wrong choice of fitting algorithm, a case study was done on a simple prismatic part with intentional variations and the algorithms that were employed in the software were reverse engineered. Based on the results of the experiments, a standardization of fitting algorithms is proposed in light of the definition provided in the standard and an interpretation of manual inspection methods. The standardized fitting algorithms developed for substitute feature fitting are then used to develop Inspection maps (i-Maps) for size, orientation and form tolerances that apply to planar feature types. A methodology for Statistical Process Control (SPC) using these i-Maps is developed by fitting the i-Maps for a batch of parts into the parent Tolerance Maps (T-Maps). Different methods of computing the i-Maps for a batch are explored such as the mean, standard deviations, computing the convex hull and doing a principal component analysis of the distribution of the individual parts. The control limits for the process and the SPC and process capability metrics are computed from inspection samples and the resulting i-Maps. Thus, a framework for statistical control of the manufacturing process is developed.

Multivariate statistical approaches for an early detection of foaming in a refinery SCOT unit

2018 ◽  
Vol 66 (8) ◽  
pp. 665-679
Author(s):  
Hassan Enam Al Mawla ◽  
Andreas Kroll
Keyword(s):  
Principal Component Analysis ◽  
Early Detection ◽  
Time Pressure ◽  
Principal Component ◽  
Component Analysis ◽  
Differential Pressure ◽  
Foam Formation ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Proper Actions

Abstract The formation of foam in amine units is an issue that plant operators and field personnel are confronted with on a regular basis. The inability to take proper actions in due time may result in plant downtime and increased emissions. Steep rises in differential pressure indicate foam formation, and are monitored manually in practice. Antifoaming agent is added in order to reduce foaming, but this is usually carried out under time pressure. Hence, plant operating authorities have expressed a strong interest in a data-driven solution capable of providing an early warning against foaming. The classical univariate alarm associated with differential pressure can be ineffective for foaming detection due to high misdetection rates and its lateness of detection. Modern univariate approaches based on pattern recognition techniques may not be suitable either for an early detection, as no universally distinctive features of differential pressure are observed prior to foaming in the present study. In this contribution, the multivariate statistical process monitoring approach based on principal component analysis (PCA) is applied to the early detection of foaming in a continuously operated Shell Claus Off-gas Treating (SCOT) unit of a major refinery in Germany. The results are extended to facilitate fully automated and adaptive modeling based on exponentially weighted recursive principal component analysis (EWRPCA).

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