Parallel Algorithm for Control Chart Pattern Recognition

Accurate control chart patterns recognition (CCPR) plays an essential role in the implementation of control charts. However, it is a challenging problem since nonrandom control chart patterns (CCPs) are normally distorted by “common process variations”. In this paper, a novel method of CCPR by integrating fuzzy support vector machine (SVM) with hybrid kernel function and genetic algorithm (GA) is proposed. Firstly, two shape features and two statistical features that do not depend on the distribution parameters and number of samples are presented to explicitly describe the characteristics of CCPs. Then, a novel multiclass method based on fuzzy SVM with a hybrid kernel function is proposed. In this method, the influence of outliers on classification accuracy of SVM-based classifiers is weakened by assigning a degree of membership for every training sample. Meanwhile, a hybrid kernel function combining Gaussian kernel and polynomial kernel is adopted to further enhance the generalization ability of the classifiers. To solve the issue of features selection and parameters optimization, GA is used to simultaneously optimize the input features subsets and parameters of fuzzy SVM-based classifier. Finally, several simulation experiments and a real example are addressed to validate the feasibility and effectiveness of the proposed methodology. And the results of simulation experiments demonstrate that it can achieve excellent performance for CCPR and outperforms other approaches, such as learning vector quantization network, multi-layer perceptron network, probability neural network, fuzzy clustering and SVM, in term of recognition accuracy. The results of the practical cases manifest that the proposed method has application potential for solving the problem of control chart interpretation in real-world.

Recognition of abnormal patterns in control charts provides clues to reveal potential quality problems in the manufacturing processes. One potentially popular approach for recognizing different control chart patterns (CCPs) is to develop heuristics based on various shape features of the patterns. The advantage of this approach is that the users can easily understand how a particular pattern is identified. However, consistency in the recognition performance is found to be considerably poor in the heuristics approach. Since shape features represent the main characteristics of the patterns in a condensed form, artificial neural network (ANN) with features extracted from the process data as input vector representation can facilitate efficient pattern recognition with a smaller network size. In this paper, a set of seven shape features is selected, whose magnitudes are independent of the process mean and standard deviation under a special representation of the sampling interval in the control chart plot. Based on these features, the CCPs are recognized using a multilayered perceptron neural network trained by back-propagation algorithm. The recognizer can recognize all the eight commonly observed CCPs. Extensive performance evaluation of this recognizer is carried out using simulated pattern data. Numerical results indicate that the developed ANN recognizer can perform well in real time process control applications with respect to both recognition accuracy and consistency.

Control chart patterns (CCPs) are the important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. In this paper we proposed a hybrid intelligent technique for recognition of the CCPs. In this technique we have used a proper set of the shape features and statistical features as the efficient characteristics of the patterns. Then we proposed a hybrid heuristic recognition system based on particle swarm optimization (PSO) to improve the generalization performance of the radial basis function neural network (RBFNN) classifier. For this purpose, we have optimized the classifier design by searching for the best value of the parameters that tune its discriminate function. The obtained results show that the proposed technique has high recognition accuracy in comparison with other works. This recognition accuracy achieves with lower training samples.

Application of the PSO–SVM model for recognition of control chart patterns

Quality control for small samples is growing in popularity due to its broad application prospects and high research value, and one of the commonly used methods for quality control nowadays is control chart pattern recognition (CCPR). Deep learning is an effective way for CCPR, but its recognition rate of control chart patterns for small samples is low and the quality control capability is limited. Therefore, to solve these problems, this paper first proposes a Perceptron-Convolutional Siamese Neural Network (PCSNN) model to improve the pattern recognition rate of small sample control chart. And then, three pattern recognition models (Convolutional Neural Network, Perceptron-Convolutional Neural Network, and PCSNN) are compared to verify the validity of the proposed model. After that, simulation experiments are conducted, and results show that the proposed PCSNN model has a high recognition rate of control chart patterns for small samples and performs well when the sample quality parameters change within a certain range. Finally, the proposed model is applied to the manufacturing process of automatic boring of enterprise gearbox shell, and its validity is verified.

Control Chart Pattern Recognition (CCPR) plays a crucial role in maintaining product quality. This paper presents some recognition models that utilizes a custom convolutional network alongside pretrained VGG19, MobileNet, and LeNet networks to identify Control Chart Patterns (CCP) for autocorrelated processes. The suggested architectures autonomously extract features from input data, in contrast to conventional methods that necessitate manual feature engineering. This study addresses the challenge of training deep networks with insufficient training data by employing transfer learning to refine a VGG19, MobileNet, and LeNet model, for a new CCPR task. The comparison of the performance between pre-trained networks and the extended convolutional network as a 2D CNN, which does not utilize transfer learning, indicates that pre-trained networks attain superior recognition accuracies with a smaller training data. On the other hand, the pretrained VGG19 demonstrates superior performance when compared to 1D CNN and conventional machine learning techniques, highlighting the advantages of utilizing transfer learning. Additionally, the utilization of pre-trained models addresses the challenge of the intricate design involved in the feature extraction component of deep networks that possess numerous hyperparameters. The suggested method has demonstrated significant potential for identifying CCPs, as evidenced by comparative experimental findings and a real-world case study.

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.

Unnatural control chart patterns (CCPs) are associated with a particular set of assignable causes for process variation. Hence, effectively recognizing CCPs can substantially narrow down the set of possible causes to be examined, and accelerate the diagnostic search. Recently, machine-learning techniques, especially the artificial neural network (ANN), have been widely used as an effective tool for CCP recognition (CCPR) tasks. Most ANN applications in CCPR have been using static supervised ANNs, such as back propagation networks (BPNs) and learning vector quantization (LVQ) networks. The false recognition problem (i.e. the patterns are misclassified) commonly encountered for these ANN-based CCPR models is mainly due to the fact that the static ANNs cannot appropriately deal with dynamic patterns, such as CCPs. In this research, a dynamic training algorithm is designed to provide an LVQ network-based CCPR model the capability to on-line recognize the dynamic CCPs that vary over time. The numerical results using simulation show that the dynamically trained LVQ network-based model proposed in this research performs much better than other ANN-based models reported in literature with respective to recognition accuracy and speed. Although this research considers the specific application of a real-time CCPR model based on an LVQ network, the proposed dynamic training algorithm could be applied to CCPR systems based on other ANN architectures in general.

A control chart pattern recognition system using a statistical correlation coefficient method

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.

Quality Management (QM) is one of the key supply chain processes. Previous studies have shown that QM provides a major contribution to the supply chain performance. One area that is essential in the managing of quality is Statistical Process Control (SPC). Control chart is one of the quality tools used in SPC for achieving process stability, controlling process and improving process by the reduction of process variations. Numerous techniques have been used to identify the presence of unnatural control chart patterns (CCPs); however, these approaches mostly focus on recognizing basic CCPs from a single type of unnatural assignable cause. In the situation where a mixture of CCPs exist, where more than one type of unnatural variation exist at the same time within the manufacturing process, this mixture of CCPs might be incorrectly classified. The Independent Component Analysis (ICA) technique has been applied recently to separate independent components from a mixture of two basic CCPs. This technique tries to maximize the statistical independence of the mixing components and uses kurtosis or a fourth-cumulant as a measure of non-Gaussianity, which implies statistical independence. Therefore, the CCP mixture separation performance and accuracy of using ICA greatly depend on kurtosis values and distributions of the basic CCPs. This paper will investigate the impact of the kurtosis values and distributions of basic CCPs on the effectiveness of the ICA-based approach in terms of being able to separate basic CCPs from a mixture, when using neural network as the pattern classifier.

In this paper, control chart pattern recognition using artificial neural networks is presented. An important motivation of this research is the growing interest in intelligent manufacturing systems, specifically in the area of Statistical Process Control (SPC). On-line automated process analysis is an important area of research since it allows the interfacing of process control with Computer Integrated Manufacturing (CIM) techniques. A back propagation artificial neural network is used to model X-bar quality control charts and identify process instability situations as specified by the Western Electric Statistical Quality Control handbook. Results indicate that the performance of the back propagation neural network is very accurate in identifying these control chart patterns. This work is significant in that the neural network output can serve as a link to process parameters in a closed-loop control system. In this way, adjustments to the process can be made on-line and quality problems averted.

This paper presents a supervised competitive learning network approach, called a fuzzy-soft learning vector quantization, for control chart pattern recognition. Unnatural patterns in control charts mean that there are some unnatural causes for variations in statistical process control (SPC). Hence, control chart pattern recognition becomes more important in SPC. In order to detect effectively the patterns for the six main types of control charts, Pham and Oztemel described a class of pattern recognizers for control charts based on the learning vector quantization (LVQ) such as LVQ, LVQ2 and LVQ-X etc. In this paper, we propose a new supervised LVQ for control charts based on a fuzzy-soft competitive learning network. The proposed fuzzy-soft LVQ (FS-LVQ) uses a fuzzy relaxation technique and simultaneously updates all neurons. It can increase correct recognition accuracy and also decrease the learning time. Comparisons between LVQ, LVQ-X and FS-LVQ are made. Numerical results show that the proposed FS-LVQ has better accuracy and less learning epochs for all neurons being completely learned than LVQ and LVQ-X. Overall, FS-LVQ is highly recommended to be used as a control chart pattern recognizer.

This paper presents a new approach to classify six anomaly types of control chart patterns (CCP), of systematic pattern, cyclic pattern, upward shift, downward shift, upward trend, and downward trend. Current CCP recognition methods use either unprocessed raw data or complex transformed features (via principal component analysis or discrete wavelet transform) as the input representation for the classifier. The objective of using selected features is not only for dimension reduction of input representation, but also implies the process of data compression. In contrast, using raw data is often computationally inefficient while using transformed features is very tedious in most cases. Therefore, owing to its computational advantage, using appropriate features of CCP to achieve good classification accuracy becomes more promising in real process implementation. In this study, using three features of CCP shows quite a competitive performance in terms of classification accuracy and computational loading. More importantly, the proposed method presented here has potential to be generalized to medical, financial, and other application of temporal data.

<span lang="EN-US">Control chart patterns (CCPs) are an essential diagnostic tool for process monitoring using statistical process control (SPC). CCPs are widely used to improve production quality in many engineering applications. The principle is to recognize the state of a process, either a stable process or a deterioration to an unstable process. It is used to significantly narrow the set of possible assignable causes by shortening the diagnostic process to improve the quality. Machine learning techniques have been widely used in CCPs. Artificial neural networks with multilayer perceptron (ANN-MLP) are one of the standard tools used for this purpose. This paper proposes an improved features selection method to select the best features as input representation for control chart patterns recognition. The results demonstrate that the proposed approach can effectively recognize CCPs even for small patterns with a mean shift of less than 1.5 sigma. The dimensional reduction was achieved by employing Relief, correlation, and Fisher algorithms (RCF) for feature selection and (ANN-MLP) as a classifier (RCF-ANN). This study provides an experimental result that compares the performance before and after dimensional reduction.</span>