Identification and quantification of concurrent control chart patterns using extreme-point symmetric mode decomposition and extreme learning machines

2015 ◽  
Vol 147 ◽  
pp. 260-270 ◽  
Author(s):  
Wen-An Yang ◽  
Wei Zhou ◽  
Wenhe Liao ◽  
Yu Guo
Keyword(s):  
Extreme Point ◽  
Control Chart ◽  
Symmetric Mode ◽  
Extreme Learning Machines ◽  
Concurrent Control ◽  
Learning Machines ◽  
Mode Decomposition ◽  
Control Chart Patterns ◽  
Chart Patterns ◽  
Identification And Quantification

A Conceptual Methodology for Recognition of Constrained Control Chart Patterns

2013 ◽  
Vol 845 ◽  
pp. 696-700
Author(s):  
Razieh Haghighati ◽  
Adnan Hassan
Keyword(s):  
Pattern Recognition ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Fault Tolerant ◽  
Constrained Control ◽  
Statistical Process ◽  
Control Chart Patterns ◽  
Control Chart Pattern ◽  
Chart Patterns

Traditional statistical process control (SPC) charting techniques were developed to monitor process status and helping identify assignable causes. Unnatural patterns in the process are recognized by means of control chart pattern recognition (CCPR) techniques. There are a broad set of studies in CCPR domain, however, given the growing doubts concerning the performance of control charts in presence of constrained data, this area has been overlooked in the literature. This paper, reports a preliminary work to develop a scheme for fault tolerant CCPR that is capable of (i) detecting of constrained data that is sampled in a misaligned uneven fashion and/or be partly lost or unavailable and (ii) accommodating the system in order to improve the reliability of recognition.

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