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Paper Information

Journal:   SCIENTIA IRANICA   2015 , Volume 22 , Number 6 (TRANSACTIONS E: INDUSTRIAL ENGINEERING); Page(s) 2527 To 2547.
 
Paper: 

MONITORING MULTIVARIATE ENVIRONMENTS USING ARTIFICIAL NEURAL NETWORK APPROACH: AN OVERVIEW

 
 
Author(s):  ATASHGAR K.*
 
* DEPARTMENT OF INDUSTRIAL ENGINEERING, IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY, TEHRAN, IRAN
 
Abstract: 

When a process shifts to an out-of-control condition, a search should be initiated to identify and eliminate the special cause (s) manifested to the technical specification (s) of the process. In the case of a process (or a product) involving several correlated technical specifications, analyzing the joint e effects of the correlated specifications is more complicated compared to a process involving only one technical specification.
Most real cases refer to processes involving more than one variable. The complexity of a solution to monitor the condition of these processes, estimate the change point and identify further knowledge leading to root-cause analysis motivated researchers to develop solutions based on Artificial Neural Networks (ANN). This paper provides, analytically, a comprehensive literature review on monitoring multivariate processes approaching artificial neural networks. Analysis of the strength and weakness of the proposed schemes, along with comparing their capabilities and properties,, are also considered. Some opportunities for new researches into monitoring multivariate environments are provided in this paper.

 
Keyword(s): ARTIFICIAL NEURAL NETWORK, MULTIVARIATE PROCESS, DIAGNOSTIC ANALYSIS, CHANGE POINT
 
 
References: 
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Click to Cite.
APA: Copy

ATASHGAR, K. (2015). MONITORING MULTIVARIATE ENVIRONMENTS USING ARTIFICIAL NEURAL NETWORK APPROACH: AN OVERVIEW. SCIENTIA IRANICA, 22(6 (TRANSACTIONS E: INDUSTRIAL ENGINEERING)), 2527-2547. https://www.sid.ir/en/journal/ViewPaper.aspx?id=508192



Vancouver: Copy

ATASHGAR K.. MONITORING MULTIVARIATE ENVIRONMENTS USING ARTIFICIAL NEURAL NETWORK APPROACH: AN OVERVIEW. SCIENTIA IRANICA. 2015 [cited 2021May11];22(6 (TRANSACTIONS E: INDUSTRIAL ENGINEERING)):2527-2547. Available from: https://www.sid.ir/en/journal/ViewPaper.aspx?id=508192



IEEE: Copy

ATASHGAR, K., 2015. MONITORING MULTIVARIATE ENVIRONMENTS USING ARTIFICIAL NEURAL NETWORK APPROACH: AN OVERVIEW. SCIENTIA IRANICA, [online] 22(6 (TRANSACTIONS E: INDUSTRIAL ENGINEERING)), pp.2527-2547. Available: https://www.sid.ir/en/journal/ViewPaper.aspx?id=508192.



 
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