Control Chart Patterns Research Articles

Effectiveness of Different Preprocessing Techniques on Classification of Various Lengths of Control Charts Patterns

Abstract Control Chart Patterns (CCPs) can be considered as time series. They are used in monitoring the control process, therefore an ability to recognize these patterns are essential in manufacturing as abnormalities can then be detected at early stage. Feeding CCPs directly to classifiers has proven unsatisfactory, especially in existence of noises. Therefore, there have been different kinds of preprocessing of CCPs to aid the classification. This research has two main objectives, first, is to study how the lengths of CCPs affect the performance of the classification. Second, the study attempts to determine the most suitable preprocessing techniques that have been applied to CCPs. Three preprocessing techniques are selected, these are Kalman filter, statistical features and symbolic representation known as Symbolic Aggregate ApproXimation (SAX). The Minimum Descriptive Length (MDL) algorithm for selecting SAX parameters is also investigated. Neural network is chosen as the tool to implement classifiers. The study concludes that longer patterns are more preferable than shorter ones. Statistical features is found as the best preprocessing techniques.

Capability of Control Chart Patterns Classifiers on Various Noise Levels

Abstract Time Series Classification is one of the areas in data mining which receives some attention recently. Control Chart Patterns (CCPs) can be considered as time series. Monitoring and recognition of CCPs is also an importance process in manufacturing. This implies that ability to classify CCPs with high accuracy is essential. This study attempts to implement CCPs classifiers which are capable of dealing with CCPs with different level of noise. Extracting image processing statistical features is adopted as preprocessing technique. The work also investigates the effect of level of noise in classification. Three different types of techniques for implementing classifiers are selected, these are Decision Tree, Neural network and an evolutionary based program, known as Self-adjusting Association Rules Generator (SARG). It was found that SARG yielded the best performance among them. To date, this study is an attempt to classify particular model of CCPs with highest level of noise.

Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features

Control charts have been widely utilized for monitoring process variation in numerous applications. Abnormal patterns exhibited by control charts imply certain potentially assignable causes that may deteriorate the process performance. Most of the previous studies are concerned with the recognition of single abnormal control chart patterns (CCPs). This paper introduces an intelligent hybrid model for recognizing the mixture CCPs that includes three main aspects: feature extraction, classifier, and parameters optimization. In the feature extraction, statistical and shape features of observation data are used in the data input to get the effective data for the classifier. A multiclass support vector machine (MSVM) applies for recognizing the mixture CCPs. Finally, genetic algorithm (GA) is utilized to optimize the MSVM classifier by searching the best values of the parameters of MSVM and kernel function. The performance of the hybrid approach is evaluated by simulation experiments, and simulation results demonstrate that the proposed approach is able to effectively recognize mixture CCPs.

Hybrid Patterns Recognition of Control Chart Based on WA-PCAPSO-SVM

Based on the analysis of the defect of traditional model, this paper proposes a new control chart pattern recognition model, which includes Wavelet Analysis (WA), Principal Component Analysis (PCA), Particle Swarm Optimization (PSO) and Support Vector Machine (SVM). WA is good to eliminate noise control chart anomaly pattern recognition of the adverse effect. PCA eliminates the redundant information of data between SVM and reduces the input dimension and computational complexity. PSO algorithm optimizes the parameters of SVM and the establishment of the optimal control chart anomaly pattern classifier can solve the problem optimal parameters of SVM. The simulation results show that the model is feasible, the results are reliable. This algorithm improves the control chart abnormal state average recognition accuracy and be used in the machining process real-time monitoring.

Recognition of Control Chart Patterns Using Imperialist Competitive Algorithm and Fuzzy Rules Approach

Traditionally, Control Chart Patterns (CCP) is widely used as a powerful method to measure, classify,analyze and interpret process data to improve the quality of products and service by detecting instabilities and justifying possible causes. In this study, we have developed an expert system that we called an expert system for control chart patterns recognition for recognition of the common types of control chart patterns (CCPs). The proposed system includes three main modules: the feature extraction module, the classifier module and the optimization module. In the feature extraction module, the multi-resolution wavelets (MRW) are proposed as the effective features for representation of CCPs. In the classifier module, the adaptive neuro-fuzzy inference system (ANFIS) is investigated. In ANFIS training, the vector of radius has a very important role for its recognition accuracy. Therefore, in the optimization module, imperialist competitive algorithm(ICA) is proposed for finding optimum vector of radius. Simulation results show that the proposed system has high recognition accuracy.

Control chart pattern recognition using an integrated model based on binary-tree support vector machine

Control charts are important tools in statistical process control for determining whether a process is in control. Furthermore, effective recognition of abnormal control chart pattern (CCP) can greatly narrow the set of possible assignable causes, significantly shortening the diagnostic process. In this article, an integrated model in which binary-tree support vector machine (BTSVM) is applied for abnormal CCP recognition is proposed. The integrated model consists of three stages. In the first stage, five statistical features and eight shape features are extracted. In the second stage, a binary-class support vector machine is used to detect abnormal CCPs. In the third stage, the BTSVM is applied to classify the detected abnormal CCPs. Additionally, the Fisher Ratio method is utilised at each node of the binary tree to design the architecture of the BTSVM. Simulation experimental results show that the proposed model is able to effectively identify the type of CCPs and that the classification accuracies of the second and the third stages are up to 100% and 98.5%, respectively. A series of contrast experiments prove that the classification accuracy of BTSVM outperforms that of one against one and one against rest for abnormal CCP classification.

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

Control chart pattern recognition (CCPR) is an important issue in statistical process control because unnatural control chart patterns (CCPs) exhibited on control charts can be associated with specific causes that adversely affect the manufacturing processes. In recent years, many machine learning techniques [e.g., artificial neural networks (ANNs) and support vector machines (SVMs)] have been successfully applied to CCPR. However, such existing research for CCPR has mostly been developed for identification of basic CCPs. Little attention has been given to the utilization of ANNs/SVMs for identification of concurrent CCPs (two or more basic CCPs occurring simultaneously) which are commonly encountered in practical manufacturing processes. In addition, these existing research for CCPR cannot provide more detailed CCP parameter information, such as shift magnitude, trend slope, cycle amplitude, etc., which is very useful for quality practitioners to search the assignable causes that give rise to the out-of-control situation. This study proposes a hybrid approach that integrates extreme-point symmetric mode decomposition (ESMD) with extreme learning machine (ELM) to identify typical concurrent CCPs and in addition to accurately quantify the major CCP parameter of the specific basic CCPs involved. The numerical results indicate that the proposed model can effectively identify not only concurrent CCPs but also basic CCPs. Meanwhile, the major CCP parameter of the identified concurrent CCP can also be accurately quantified.

Identification of the Control Chart Patterns Using the Optimized Adaptive Neuro-Fuzzy Inference System

Unnatural patterns in the control charts can be associated with a specific set of assignable causes for process variation. Hence pattern recognition is very useful in identifying process problem. This paper presents a novel hybrid intelligent method for recognition of common types of control chart patterns (CCPs). The proposed method includes three main modules: the feature extraction module, the classifier module and the optimization module. In the feature extraction module, a proper set of the shape features and statistical features is proposed as the efficient characteristic of the patterns. In the classifier module adaptive neuro-fuzzy inference system (ANFIS) is investigated. In ANFIS training, the vector of radius has very important role for its recognition accuracy. Therefore, in the optimization module, cuckoo optimization algorithm (COA) is proposed for finding of optimum vector of radius. Simulation results show that the proposed system has high recognition accuracy.

Abnormal Control Chart Pattern Classification Optimisation Using Multi Layered Perceptron

In today's industry, control charts are widely used to monitor production process. The abnormal patterns of a quality control chart could reveal problems that occur in the process. In the recent years, as an alternative of the traditional process quality management methods, such as Shewhart Statistical Process Control (SPC), Artificial Neural Networks (ANN) have been widely used to recognize the abnormal pattern of control charts. Various types of patterns are observed in control charts. Identification of these Control Chart Patterns (CCPs) can provide clues to potential quality problems in the manufacturing process. Each type of control chart pattern has its own geometric shape and various related features can represent this shape. Feature-based approaches can facilitate efficient pattern recognition since extracted shape features represent the main characteristics of the patterns in a condensed form. The objective of this study was to evaluate the relative performance of a feature-based CCP recognizer compared with the raw data-based recognizer. The study focused on recognition of six commonly researched CCPs plotted on the Shewhart X-bar chart. The ANN-based CCP recognizer trained using the nine shape features resulted in significantly better performance and generalization compared with the raw data-based recognizer.

A multi-stage control chart pattern recognition scheme based on independent component analysis and support vector machine

Recognition of unnatural control chart patterns (CCPs) is an important issue because the unnatural CCPs can be associated with specific assignable causes negatively affecting the manufacturing process. By assuming that an unnatural CCP is a combination of normal pattern and process disturbance, a multi-stage control chart pattern recognition scheme which integrates independent component analysis (ICA) and support vector machine (SVM) is proposed in this study. The proposed multi-stage ICA-SVM scheme first uses ICA to extract independent components (ICs) from the monitoring process data containing CCPs. The normal pattern and process disturbance hidden in the process data can be discovered in the ICs. Then, the IC representing the process disturbance can be identified. Finally, the identified IC and the data of the monitoring process are used as input variables to develop three different SVM models for CCP recognition. The simulation results show that the proposed multi-stage ICA-SVM scheme not only produces accurate and stable recognition results but also has better classification accuracy than four competing models.

Recognition of Concurrent Control Chart Patterns by Integrating ICA and SVM

Control chart patterns (CCPs) recognition is an important issue in statistica l process control, since abnormal CCPs exhibited in control charts can be associated with certain assignable causes and the n help to quickly eliminate assignable causes of process variation. Most of the existing studies assume that the observed process data needs to be recognized are basic types of abnormal CCPs. However, in practical situations, the observed process data may be concurrent CCPs which are mixed together by two basic CCPs. In this study, an integrated scheme using independent component analysis (ICA) and support vector machine (SVM) is proposed for recognizing concurrent CCPs. The proposed ICA-SVM scheme initially uses ICA with concurrent patterns for generating independent components (ICs). The hidden basic patterns of the concurrent patterns can be discovered in these ICs. The ICs are then used as input variables for the SVM for building a CCP recognition model. Experimental results reveal that the proposed ICA-SVM scheme can produce accurate and stable recognition results. It outperforms th e three comparison models and is able to effectively recognize concurrent control chart patterns.

A weighted support vector machine method for control chart pattern recognition

Manual inspection and evaluation of quality control data is a tedious task that requires the undistracted attention of specialized personnel. On the other hand, automated monitoring of a production process is necessary, not only for real time product quality assessment, but also for potential machinery malfunction diagnosis. For this reason, control chart pattern recognition (CCPR) methods have received a lot of attention over the last two decades. Current state-of-the-art control monitoring methodology includes K charts which are based on support vector machines (SVM). Although K charts have some profound benefits, their performance deteriorate when the learning examples for the normal class greatly outnumbers the ones for the abnormal class. Such problems are termed imbalanced and represent the vast majority of the real life control pattern classification problems. Original SVM demonstrate poor performance when applied directly to these problems. In this paper, we propose the use of weighted support vector machines (WSVM) for automated process monitoring and early fault diagnosis. We show the benefits of WSVM over traditional SVM, compare them under various fault scenarios. We evaluate the proposed algorithm in binary and multi-class environments for the most popular abnormal quality control patterns as well as a real application from wafer manufacturing industry.

An innovative price plan monitoring and advisory system: A case study of mobile telecom service in Thailand

Article history: Received October 23,2013 Received in revised format November 25, 2013 Accepted November 27, 2013 Available online November 27 2013 An innovative price plan monitoring and advisory system simulates subscriber usage consumption for offering suitable price plan. The aim of this paper is to develop the decision support system by using Statistical Process Control (SPC) to identify subscriber usage behavior and provide critical visibility into subscriber consumption to detect their inappropriate usage especially in exceeding usage. To explore subscriber usage behavior, a forecasting model and a regression is employed to identify related factors and predictive usage model. The innovative price plan monitoring and advisory system has been verified and validated with one of the largest telecommunication company in Thailand. Using decision support system with effective control chart and real subscriber behavior pattern help mobile network operator grow their revenues and profits by offering an appropriate price plan as well as improve subscriber experience with more flexible choice to meet their individual usage consumption needs. © 2014 Growing Science Ltd. All rights reserved.

A Conceptual Methodology for Recognition of Constrained Control 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.

A Hybrid Learning-Based Model for Simultaneous Monitoring of Process Mean and Variance

Unnatural patterns exhibited on process mean and variance control charts can be associated separately with different assignable causes. Quick and accurate knowledge of the type of control chart patterns (CCPs), either because of process mean or variance, can greatly facilitate identification of assignable causes. Over the past few decades, however, process mean and variance CCPs are seldom studied simultaneously in the statistical process control literature. This study proposes a hybrid learning-based model for simultaneous monitoring of process mean and variance CCPs. In this model, a self-organization map neural network-based quantization error control chart is responsible for detecting the out-of-control signals, a discrete particle swarm optimization-based selective ensemble of back-propagation networks is responsible for classifying the detected out-of-control signals into categories of mean and/or variance abnormality, and two discrete particle swarm optimization-based selective ensembles of learning vector quantization networks are responsible for further identifying the detected mean and variance out-of-control signals as one of the specific CCP types, respectively. Extensive simulations indicate that the proposed hybrid learning-based model outperforms other existing approaches in detecting mean and variance changes, while also capable of CCP recognition. In addition, a case study is conducted to demonstrate how the proposed hybrid learning-based model can function as an effective tool for monitoring mean and variance simultaneously. Copyright © 2013 John Wiley & Sons, Ltd.

Autoregressive coefficient-invariant control chart pattern recognition in autocorrelated manufacturing processes using neural network ensemble

Pattern recognition is an important issue in statistical process control because unnatural control chart patterns (CCPs) exhibited on control charts can be associated with specific causes that adversely affect the manufacturing process. In recent years, many machine learning techniques [e.g., artificial neural networks (ANNs) and support vector machine (SVM)] have been successfully used to the CCP recognition (CCPR) in autocorrelated manufacturing processes. However, these existing researches can only detect and classify unnatural CCPs but do not provide more detailed process information, for example the autocorrelation level, which would be very useful for quality practitioners to search the assignable causes that give rise to the out-of-control situation. This study proposes a neural network ensemble-enabled autoregressive coefficient-invariant CCPR model for on-line recognition of seven typical types of unnatural CCP, assuming that the process observations are AR(1) correlated over time with unknown constant autoregressive coefficient. In this model, each individual back propagation network (BPN) is trained to recognize only CCPs with the specific autoregressive coefficient of the underlying process, while the outputs of all these individual BPNs are combined via a learning vector quantization network (LVQN). The experimental results indicate that the proposed CCPR model can not only accurately recognize the specific CCP types but also efficiently identify the autoregressive coefficient of the underlying autocorrelated manufacturing process. Empirical comparisons also show that the proposed CCPR model outperform other existing CCPR approaches in literature. In addition, a demonstrative application is provided to illustrate the utilization of the proposed CCPR model to the CCPR in autocorrelated manufacturing processes.

Statistical process control using optimized neural networks: A case study

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy.

Recognition and classification of single and concurrent unnatural patterns in control charts via neural networks and fitted line of samples

The correct, prompt recognition and analysis of unnatural and significant patterns in Schewhart’s control charts are very important since they remind out-of-control conditions. In fact, pattern extraction increases the sensitivity of charts when identifying out of control conditions. Artificial neural networks have been used to identify unnatural patterns in many research studies due to their high efficiency in pattern recognition. In most of such studies, there is a significant risk of misclassification of highly sensitive patterns. To put it more clearly, the proposed models offered for the recognition of patterns with low parametric coefficients are mistaken. This study, offers a model for the recognition and analysis of basic patterns in process control charts using LVQ and MLP networks along with a fitted line analysis. In this model, not only does risk of misclassification at different levels of sensitivity decrease remarkably, but there will also be the possibility for recognition and analysis when basic pattern occur simultaneously. The efficiency and effectiveness of the model are shown by conducting tests based on simulation.

Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines

Statistical process control charts have been widely utilized for monitoring process variation in many applications. Nonrandom patterns exhibited by control charts imply certain potential assignable causes that may deteriorate the process performance. Though some effective approaches to recognition of control chart patterns (CCPs) have been developed, most of them only focus on recognition and analysis of single patterns. A hybrid approach by integrating wavelet transform and improved particle swarm optimization-based support vector machine (P-SVM) for on-line recognition of concurrent CCPs is developed in this paper. A statistical correlation coefficient is used to determine whether the input pattern is a single or concurrent CCP. Based on wavelet transform, a raw concurrent pattern signal is decomposed into two basic pattern signals, which can be recognized by multiclass SVMs. The performance of the hybrid approach is evaluated by simulation experiments, and numerical and graphical results are provided to demonstrate that the proposed approach can perform effectively and efficiently in on-line CCP recognition task.

WITHDRAWN: Recognition of control chart pattern using Gbest-guided artificial bee colony algorithm

WITHDRAWN: Recognition of control chart pattern using Gbest-guided artificial bee colony algorithm