scholarly journals Fuzzy logic based assignable cause diagnosis using control chart patterns

2010 ◽  
Vol 180 (17) ◽  
pp. 3258-3272 ◽  
Author(s):  
Kudret Demirli ◽  
Sujikumar Vijayakumar
Keyword(s):  
Fuzzy Logic ◽  
Control Chart ◽  
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.

scholarly journals Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Shi-wang Hou ◽  
Shunxiao Feng ◽  
Hui Wang
Keyword(s):  
Fuzzy Logic ◽  
Control Chart ◽  
Fuzzy Inference ◽  
Recognition Method ◽  
Inference System ◽  
Characteristic Numbers ◽  
Control Chart Patterns ◽  
Intelligent Process ◽  
Chart Patterns ◽  
Method Point

Locating the assignable causes by use of the abnormal patterns of control chart is a widely used technology for manufacturing quality control. If there are uncertainties about the occurrence degree of abnormal patterns, the diagnosis process is impossible to be carried out. Considering four common abnormal control chart patterns, this paper proposed a characteristic numbers based recognition method point by point to quantify the occurrence degree of abnormal patterns under uncertain conditions and a fuzzy inference system based on fuzzy logic to calculate the contribution degree of assignable causes with fuzzy abnormal patterns. Application case results show that the proposed approach can give a ranked causes list under fuzzy control chart abnormal patterns and support the abnormity eliminating.

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.

Control chart pattern clustering using a new self-organizing spiking neural network

2008 ◽  
Vol 222 (10) ◽  
pp. 1201-1211 ◽  
Author(s):  
D T Pham ◽  
M S Packianather ◽  
E Y A Charles
Keyword(s):  
Neural Network ◽  
Control Chart ◽  
Neural Network Model ◽  
Spiking Neurons ◽  
Spiking Neural Network ◽  
Pattern Clustering ◽  
Control Chart Patterns ◽  
Chart Patterns ◽  
Spiking Neural Network Model ◽  
Self Organizing

This paper focuses on the architecture and learning algorithm associated with using a new self-organizing delay adaptation spiking neural network model for clustering control chart patterns. This temporal coding spiking neural network model employs a Hebbian-based rule to shift the connection delays instead of the previous approaches of delay selection. Here the tuned delays compensate the differences in the input firing times of temporal patterns and enables them to coincide. The coincidence detection capability of the spiking neuron has been utilized for pattern clustering. The structure of the network is similar to that of a Kohonen self-organizing map (SOM) except that the output layer neurons are coincidence detecting spiking neurons. An input pattern is represented by the neuron that is the first to fire among all the competing spiking neurons. Clusters within the input data are identified with the location of the winning neurons and their firing times. The proposed self-organized delay adaptation spiking neural network (SODA_SNN) has been utilized to cluster control chart patterns. The trained network obtained an average clustering accuracy of 96.1 per cent on previously unseen test data. This was achieved with a network of 8 × 8 spiking neurons trained for 20 epochs containing 1000 training examples. The improvement in clustering accuracy achieved by the proposed SODA_SNN on the unseen test data was twice as much as that on the training data when compared to the SOM.

Recognition of control chart patterns using an intelligent technique

2013 ◽  
Vol 13 (5) ◽  
pp. 2970-2980 ◽  
Author(s):  
Ata Ebrahimzadeh ◽  
Jalil Addeh ◽  
Vahid Ranaee
Keyword(s):  
Control Chart ◽  
Intelligent Technique ◽  
Control Chart Patterns ◽  
Chart Patterns
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