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.

scholarly journals Fuzzy Heuristics and Decision Tree for Classification of Statistical Feature-Based Control Chart Patterns

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 110
Author(s):  
Munawar Zaman ◽  
Adnan Hassan
Keyword(s):  
Control Chart ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Classification And Regression Tree ◽  
Fuzzy Classifier ◽  
Inference System ◽  
Machine Learning Classification ◽  
Control Chart Pattern ◽  
Chart Patterns ◽  
Fuzzy Heuristics

Monitoring manufacturing process variation remains challenging, especially within a rapid and automated manufacturing environment. Problematic and unstable processes may produce distinct time series patterns that could be associated with assignable causes for diagnosis purpose. Various machine learning classification techniques such as artificial neural network (ANN), classification and regression tree (CART), and fuzzy inference system have been proposed to enhance the capability of traditional Shewhart control chart for process monitoring and diagnosis. ANN classifiers are often opaque to the user with limited interpretability on the classification procedures. However, fuzzy inference system and CART are more transparent, and the internal steps are more comprehensible to users. There have been limited works comparing these two techniques in the control chart pattern recognition (CCPR) domain. As such, the aim of this paper is to demonstrate the development of fuzzy heuristics and CART technique for CCPR and compare their classification performance. The results show the heuristics Mamdani fuzzy classifier performed well in classification accuracy (95.76%) but slightly lower compared to CART classifier (98.58%). This study opens opportunities for deeper investigation and provides a useful revisit to promote more studies into explainable artificial intelligence (XAI).

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.

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

scholarly journals Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

2021 ◽  
Vol 9 (1) ◽  
pp. 49
Author(s):  
Tanja Brcko ◽  
Andrej Androjna ◽  
Jure Srše ◽  
Renata Boć
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Decision Model ◽  
Fuzzy Inference ◽  
Weather Conditions ◽  
Target Vessel ◽  
Planning Tool ◽  
Inference System ◽  
International Regulations ◽  
Input Variables

The application of fuzzy logic is an effective approach to a variety of circumstances, including solutions to maritime anti-collision problems. The article presents an upgrade of the radar navigation system, in particular, its collision avoidance planning tool, using a decision model that combines dynamic parameters into one decision—the collision avoidance course. In this paper, a multi-parametric decision model based on fuzzy logic is proposed. The model calculates course alteration in a collision avoidance situation. First, the model collects input data of the target vessel and assesses the collision risk. Using time delay, four parameters are calculated for further processing as input variables for a fuzzy inference system. Then, the fuzzy logic method is used to calculate the course alteration, which considers the vessel’s safety domain and International Regulations for Preventing Collisions at Sea (COLREGs). The special feature of the decision model is its tuning with the results of the database of correct solutions obtained with the manual radar plotting method. The validation was carried out with six selected cases simulating encounters with the target vessel in the open sea from different angles and at any visibility. The results of the case studies have shown that the decision model computes well in situations where the own vessel is in a give-way position. In addition, the model provides good results in situations when the target vessel violates COLREG rules. The collision avoidance planning tool can be automated and serve as a basis for further implementation of a model that considers the manoeuvrability of the vessels, weather conditions, and multi-vessel encounter situations.

scholarly journals Traffic Type Recognition Method for Unknown Protocol—Applying Fuzzy Inference

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 36
Author(s):  
Sang-Won Kim ◽  
Kee-Cheon Kim
Keyword(s):  
Fuzzy Inference ◽  
Static Field ◽  
Theoretical Background ◽  
Alignment Algorithm ◽  
Traffic Classification ◽  
Recognition Method ◽  
Inference System ◽  
Traffic Characteristics ◽  
Sequence Alignment Algorithm ◽  
Low Performance

In this paper, we propose a system that can recognize traffic types without prior knowledge of static features such as protocol header information by combining protocol analysis based on an ecological sequence alignment algorithm in a bioinformatics and fuzzy inference system. The algorithm proposed in this paper obtained up to a 91% level of performance at a similar level to several existing algorithms in experiments using datasets containing various types of traffic. In addition, it showed an excellent accuracy of 82.5% or more even under severe conditions that lowered the amount of data to a level of at least 40% or only included data in the middle of the traffic. This shows that the problem of dependence on initial data that frequently occurs in existing machine learning and deep learning-based traffic classification algorithms does not appear in the proposed algorithm. Furthermore, based on the ability to directly extract traffic characteristics without being dependent on static field values, it has secured the ability to respond with a small number of data by taking advantage of the flexibility of the membership function of the fuzzy inference engine. Through this, the applicability to low-power and low-performance environments such as IoT networks was confirmed. In this paper, we describe in detail the theoretical background for constructing such an algorithm and relevant experiments and considerations for actual verification.

Sign in / Sign up

Export Citation Format

Share Document