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.

scholarly journals Statistical Process Control with Intelligence Based on the Deep Learning Model

2019 ◽  
Vol 10 (1) ◽  
pp. 308 ◽  
Author(s):  
Tao Zan ◽  
Zhihao Liu ◽  
Zifeng Su ◽  
Min Wang ◽  
Xiangsheng Gao ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Manufacturing Industry ◽  
Short Term Memory ◽  
Statistical Process ◽  
Control Chart Pattern ◽  
Chart Patterns

Statistical process control (SPC) is an important tool of enterprise quality management. It can scientifically distinguish the abnormal fluctuations of product quality. Therefore, intelligent and efficient SPC is of great significance to the manufacturing industry, especially in the context of industry 4.0. The intelligence of SPC is embodied in the realization of histogram pattern recognition (HPR) and control chart pattern recognition (CCPR). In view of the lack of HPR research and the complexity and low efficiency of the manual feature of control chart pattern, an intelligent SPC method based on feature learning is proposed. This method uses multilayer bidirectional long short-term memory network (Bi-LSTM) to learn the best features from the raw data, and it is universal to HPR and CCPR. Firstly, the training and test data sets are generated by Monte Carlo simulation algorithm. There are seven histogram patterns (HPs) and nine control chart patterns (CCPs). Then, the network structure parameters and training parameters are optimized to obtain the best training effect. Finally, the proposed method is compared with traditional methods and other deep learning methods. The results show that the quality of extracted features by multilayer Bi-LSTM is the highest. It has obvious advantages over other methods in recognition accuracy, despite the HPR or CCPR. In addition, the abnormal patterns of data in actual production can be effectively identified.

Textile Processing Anomaly Detection Based on Time Domain Features and GA-SVM

2015 ◽  
Vol 740 ◽  
pp. 706-713
Author(s):  
Jian Guo Yang ◽  
Lan Xu ◽  
Zhi Jun Lu ◽  
Qian Xiang ◽  
Bin Liu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Process Control ◽  
Anomaly Detection ◽  
Control Chart ◽  
Time Domain ◽  
Control Charts ◽  
Detection Accuracy ◽  
Textile Processing ◽  
Statistical Process ◽  
Control Chart Pattern

Demands of automatic recognition of abnormal patterns in control charts have been increasing nowadays in manufacturing process. Control chart pattern recognition is an important statistical process control tool used to determine whether a process is run in its intended range or not and eliminate the potential attribution factors as far as possible according to the abnormal condition shown in the control chart. This paper uses the time domain features as input vector and genetic algorithm to obtain the optimal parameters of SVM in a self-adapted manner. Design anomaly detection model for dynamic process is made to realize control chart pattern recognition under the complex condition. The experimental results show that the proposed approach method has higher detection accuracy and stronger generalization ability than other methods, so it is more suitable for quality control in production field.

scholarly journals End-to-End Control Chart Pattern Classification Using a 1D Convolutional Neural Network and Transfer Learning

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1484
Author(s):  
Chuen-Sheng Cheng ◽  
Ying Ho ◽  
Tzu-Cheng Chiu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Pattern Recognition ◽  
Process Control ◽  
Convolutional Neural Network ◽  
Statistical Process Control ◽  
Control Chart ◽  
Statistical Process ◽  
Random Patterns ◽  
Control Chart Pattern

Control charts are an important tool in statistical process control (SPC). They have been commonly used for monitoring process variation in many industries. Recognition of non-random patterns is an important task in SPC. The presence of non-random patterns implies that a process is affected by certain assignable causes, and some corrective actions should be taken. In recent years, a great deal of research has been devoted to the application of machine learning (ML) based approaches to control chart pattern recognition (CCPR). However, there are some gaps that hinder the application of the CCPR methods in practice. In this study, we applied a control chart pattern recognition method based on an end-to-end one-dimensional convolutional neural network (1D CNN) model. We proposed some methods to generate datasets with high intra-class diversity aiming to create a robust classification model. To address the data scarcity issue, some data augmentation operations suitable for CCPR were proposed. This study also investigated the usefulness of transfer learning techniques for the CCPR task. The pre-trained model using normally distributed data was used as a starting point and fine-tuned on the unknown non-normal data. The performance of the proposed approach was evaluated by real-world data and simulation experiments. Experimental results indicate that our proposed method outperforms the traditional machine learning methods and could be a promising tool to effectively classify control chart patterns. The results and findings of this study are crucial for the further realization of smart statistical process control.

OPTIMIZING FEEDFORWARD NEURAL NETWORKS FOR CONTROL CHART PATTERN RECOGNITION THROUGH GENETIC ALGORITHMS

2004 ◽  
Vol 18 (02) ◽  
pp. 75-99 ◽  
Author(s):  
RUEY-SHIANG GUH
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Population Size ◽  
Control Chart ◽  
Network Architecture ◽  
Control Charts ◽  
Back Propagation ◽  
Statistical Process ◽  
Control Chart Pattern

Pattern recognition is an important issue in statistical process control (SPC) because unnatural patterns exhibited by control charts can be associated with specific assignable causes adversely affecting the process. Artificial neural networks have been widely investigated as an effective approach to control chart pattern (CCP) recognition in recent years. However, an overwhelming majority of these applications has used trial-and-error experiments to determine the network architecture and training parameters, which are crucial to the performance of the network. In this paper, the genetic algorithm (GA) is used to evolve the configuration and the training parameter set of the neural network to solve the online CCP recognition problem. Numerical results are provided that indicate that the proposed GA can evolve neural network architecture while simultaneously determining training parameters to maximize efficiently the performance of the online CCP recognizers. Because the population size is a major parameter of GA processing speed, an investigation was also conducted to identify the effects of the population size on the performance of the proposed GA. This research further confirms the feasibility of using GA to evolve neural networks. Although a back-propagation-based CCP recognizer is the particular application presented here, the proposed GA methodology can be applied to neural networks in general.

A control chart pattern recognition methodology for controlling information technology-enabled service (ITeS) process customer complaints

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Boby John
Keyword(s):  
Information Technology ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Control Chart ◽  
Control Charts ◽  
Service Industry ◽  
Content Type ◽  
Customer Complaints ◽  
Control Chart Pattern ◽  
Chart Patterns

PurposeThe purpose of this paper is to develop a control chart pattern recognition methodology for monitoring the weekly customer complaints of outsourced information technology-enabled service (ITeS) processes.Design/methodology/approachA two-step methodology is used to classify the processes as having natural or unnatural variation based on past 20 weeks' customer complaints. The step one is to simulate data on various control chart patterns namely natural variation, upward shift, upward trend, etc. Then a deep learning neural network model consisting of two dense layers is developed to classify the patterns as of natural or unnatural variation.FindingsThe validation of the methodology on telecom vertical processes has correctly detected unnatural variations in two terminated processes. The implementation of the methodology on banking and financial vertical processes has detected unnatural variation in one of the processes. This helped the company management to take remedial actions, renegotiate the deal and get it renewed for another period.Practical implicationsThis study provides valuable information on controlling information technology-enabled processes using pattern recognition methodology. The methodology gives a lot of flexibility to managers to monitor multiple processes collectively and avoids the manual plotting and interpretation of control charts.Originality/valueThe application of control chart pattern recognition methodology for monitoring service industry processes are rare. This is an application of the methodology for controlling information technology-enabled processes. This study also demonstrates the usefulness of deep learning techniques for process control.

Control chart pattern recognition using a new type of self-organizing neural network

1998 ◽  
Vol 212 (2) ◽  
pp. 115-127 ◽  
Author(s):  
D T Pham ◽  
A B Chan
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Control Chart ◽  
Control Charts ◽  
Statistical Process ◽  
The Neural Network ◽  
New Type ◽  
Comparison Of The Results ◽  
Control Chart Pattern ◽  
Self Organizing

Control charts as used in statistical process control can exhibit six principal types of patterns: normal, cyclic, increasing trend, decreasing trend, upward shift and downward shift. Apart from normal patterns, all the other patterns indicate abnormalities in the process that must be corrected. Accurate and speedy detection of such patterns is important to achieving tight control of the process and ensuring good product quality. This paper describes a new type of neural network for control chart pattern recognition. The neural network is self-organizing and can learn to recognize new patterns in an on-line incremental manner. The key feature of the proposed neural network is the criterion employed to select the firing neuron, i.e. the neuron indicating the pattern class. The paper gives a comparison of the results obtained using the proposed network and those for other self-organizing networks employing a different firing criterion.

scholarly journals Monitoring of mechanical sugarcane harvesting through control charts

2015 ◽  
Vol 35 (6) ◽  
pp. 1079-1092 ◽  
Author(s):  
Murilo A. Voltarelli ◽  
Rouverson P. da Silva ◽  
Cristiano Zerbato ◽  
Carla S. S. Paixão ◽  
Tiago de O. Tavares
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Grinding Wheel ◽  
Individual Values ◽  
Statistical Process ◽  
Minas Gerais State ◽  
Weighted Moving Average

ABSTRACT Statistical process control in mechanized farming is a new way to assess operation quality. In this sense, we aimed to compare three statistical process control tools applied to losses in sugarcane mechanical harvesting to determine the best control chart template for this quality indicator. Losses were daily monitored in farms located within Triângulo Mineiro region, in Minas Gerais state, Brazil. They were carried over a period of 70 days in the 2014 harvest. At the end of the evaluation period, 194 samples were collected in total for each type of loss. The control charts used were individual values chart, moving average and exponentially weighted moving average. The quality indicators assessed during sugarcane harvest were the following loss types: full grinding wheel, stumps, fixed piece, whole cane, chips, loose piece and total losses. The control chart of individual values is the best option for monitoring losses in sugarcane mechanical harvesting, as it is of easier result interpretation, in comparison to the others.

Control Chart Pattern Recognition Using Combinations of Multi-Layer Perceptrons and Learning-Vector-Quantization Neural Networks

1993 ◽  
Vol 207 (2) ◽  
pp. 113-118 ◽  
Author(s):  
D T Pham ◽  
E Oztemel
Keyword(s):  
Pattern Recognition ◽  
Control Chart ◽  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Composite Pattern ◽  
Pattern Recognition Systems ◽  
Network Modules ◽  
Recognition Systems ◽  
Control Chart Pattern ◽  
Chart Patterns

Pattern recognition systems made up of independent multi-layer perceptrons and learning-vector-quantization neural network modules have been developed for classifying control chart patterns. These composite pattern recognition systems have better classification capabilities than their individual modules. The paper describes the structures of these pattern recognition systems and the results obtained on using them.

scholarly journals Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yuehjen E. Shao ◽  
Po-Yu Chang ◽  
Chi-Jie Lu
Keyword(s):  
Neural Network ◽  
Process Control ◽  
Control Chart ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Two Stage ◽  
Accurate Identification ◽  
Statistical Process ◽  
Control Chart Patterns ◽  
Chart Patterns

The effective controlling and monitoring of an industrial process through the integration of statistical process control (SPC) and engineering process control (EPC) has been widely addressed in recent years. However, because the mixture types of disturbances are often embedded in underlying processes, mixture control chart patterns (MCCPs) are very difficult for an SPC-EPC process to identify. This can result in problems when attempting to determine the underlying root causes of process faults. Additionally, a large number of categories of disturbances may be present in a process, but typical single-stage classifiers have difficulty in identifying large numbers of categories of disturbances in an SPC-EPC process. Therefore, we propose a two-stage neural network (NN) based scheme to enhance the accurate identification rate (AIR) for MCCPs by performing dimension reduction on disturbance categories. The two-stage scheme includes a combination of a NN, support vector machine (SVM), and multivariate adaptive regression splines (MARS). Experimental results reveal that the proposed scheme achieves a satisfactory AIR for identifying MCCPs in an SPC-EPC system.

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