Recognition of control chart patterns using swarm intelligence and neural networks based on the statistical and shape features

2010 ◽  
Vol 6 (1) ◽  
pp. 80-85 ◽  
Author(s):  
Ataollah Ebrahimzadeh ◽  
Vahid Ranaee
Keyword(s):  
Neural Networks ◽  
Swarm Intelligence ◽  
Control Chart ◽  
Shape Features ◽  
Control Chart Patterns ◽  
Chart Patterns

An Assessment of Imbalanced Control Chart Pattern Recognition by Artificial Neural Networks

2022 ◽  
pp. 683-702
Author(s):  
Ramazan Ünlü
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Control Chart ◽  
Minority Class ◽  
Control Chart Patterns ◽  
Abnormal Pattern ◽  
Weighting Strategy ◽  
Control Chart Pattern ◽  
Chart Patterns ◽  
Normal Class

Manual detection of abnormality in control data is an annoying work which requires a specialized person. Automatic detection might be simpler and effective. Various methodologies such as ANN, SVM, Fuzzy Logic, etc. have been implemented into the control chart patterns to detect abnormal patterns in real time. In general, control chart data is imbalanced, meaning the rate of minority class (abnormal pattern) is much lower than the rate of normal class (normal pattern). To take this fact into consideration, authors implemented a weighting strategy in conjunction with ANN and investigated the performance of weighted ANN for several abnormal patterns, then compared its performance with regular ANN. This comparison is also made under different conditions, for example, abnormal and normal patterns are separable, partially separable, inseparable and the length of data is fixed as being 10,20, and 30 for each. Based on numerical results, weighting policy can better predict in some of the cases in terms of classifying samples belonging to minority class to the correct class.

An Assessment of Imbalanced Control Chart Pattern Recognition by Artificial Neural Networks

2020 ◽  
pp. 240-258
Author(s):  
Ramazan Ünlü
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Control Chart ◽  
Minority Class ◽  
Control Chart Patterns ◽  
Abnormal Pattern ◽  
Weighting Strategy ◽  
Control Chart Pattern ◽  
Chart Patterns ◽  
Normal Class

Manual detection of abnormality in control data is an annoying work which requires a specialized person. Automatic detection might be simpler and effective. Various methodologies such as ANN, SVM, Fuzzy Logic, etc. have been implemented into the control chart patterns to detect abnormal patterns in real time. In general, control chart data is imbalanced, meaning the rate of minority class (abnormal pattern) is much lower than the rate of normal class (normal pattern). To take this fact into consideration, authors implemented a weighting strategy in conjunction with ANN and investigated the performance of weighted ANN for several abnormal patterns, then compared its performance with regular ANN. This comparison is also made under different conditions, for example, abnormal and normal patterns are separable, partially separable, inseparable and the length of data is fixed as being 10,20, and 30 for each. Based on numerical results, weighting policy can better predict in some of the cases in terms of classifying samples belonging to minority class to the correct class.

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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