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

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

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.

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.

Blind source separation and feature extraction in concurrent control charts pattern recognition: Novel analyses and a comparison of different methods

Wind speed prediction has been used in various fields such as Satellite launch, Air traffic control, Weather forecasting etc. Wind speed can be calculated by various atmospheric variables such as temperature, humidity, pressure, wind direction, etc. A number of methods have been proposed by various researchers to predict the wind speed. During the last few years a lot of research has been carried out to forecast the wind speed using several mathematical and biological methods. This paper first reviews various wind speed prediction techniques. It then explores the use of various multiclass Support Vector Machine (SVM) algorithms for wind speed prediction. It has been shown that multiclass Directed Acyclic Graph based Support Vector Machine algorithm produces better results than other multiclass SVM algorithms.

Fuzzy logic based assignable cause diagnosis using control chart patterns

Fast control chart pattern recognition aids in instantaneous detection of abnormal functioning of a system. In this paper, we present a parallel algorithm for fast control chart pattern recognition. It addresses three major issues of control chart pattern recognition: (i) transparency (ii) accuracy and (iii) fast detection of abnormal patterns. The algorithm uses novel shape features extracted from a control chart pattern (CCP) instead of the unprocessed CCP data or its statistical properties. These shape features can be extracted in parallel. A parallel algorithm that is based on distributed and synergistic neural network structure for recognition of CCPs is described. The paper presents the results of analyzing several hundred control chart patterns and gives a comparison with those reported in previous work.

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.

Recently, focus on maintenance strategies has been shifted towards prognostic health management (PHM) and a number of state of the art algorithms based on data-driven prognostics have been developed to predict the health states of degrading components based on sensory data. Amongst these algorithms, Multiclass Support Vector Machines (MC-SVM) has gained popularity due to its relatively high classification accuracy, ability to classify multiple patterns and capability to handle noisy / incomplete data. However, its application is limited by the difficulty in determining the required kernel function and penalty parameters. To address this problem, this paper proposes a hybrid differential evolution – particle swarm optimization (DE-PSO) algorithm to optimize the MC-SVM kernel function and penalty parameters. The differential algorithm (DE) obtains the search limit for the SVM parameters, while the particle swarm optimization algorithm (PSO) determines the global optimum parameters for a given training data set. Since degrading machinery components display several degradation stages in their lifetime, the MC-SVM trained with optimum parameters are used to estimate the health states of a degrading machinery component, from which the remaining useful life (RUL) is predicted. This method improves the classification accuracy of MC-SVM in predicting the health states of a machinery component and consequently increases the accuracy of RUL predictions. The feasibility of the method is validated using bearing prognostic run-to-failure data obtained from NASA public data repository. A comparative study between MC-SVM with parameters obtained using simple grid search with n-fold cross validation and MC- SVM with DE-PSO based on prognostic performance metrics reveals that the proposed method has better performance, with all the cases considered falling within a 10 % error margin. The method also outperforms other soft computing methods proposed in literature.

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.

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.

A control chart is a tool for statistical process control (SPC) used to determine variations in manufacturing processes resulting from common or assignable causes. The presence of unnatural patterns in control charts is an indication that the process has been influenced by assignable causes, and corrective actions must be taken. However, the assumption of uncorrelated or independent observations is not suitable for all of the characteristics pertaining to a product. This study presents a method with which to improve the accuracy of pattern recognition in control charts, through the autocorrelation of product characteristics. Particularly, we developed an artificial algorithm-based machine learning model to recognize unnatural patterns and processes in AR(1) simultaneously. The proposed method integrates an artificial immune system and support vector machine within a recognition system. This study evaluated the accuracy with which four patterns could be recognized. The four patterns include trends, sudden shifts, cyclic patterns, and normal patterns. Identifying unnatural patterns can greatly narrow the range of possible causes that must be investigated, thereby speeding up the diagnostic processes. Key words: Pattern recognition, artificial immune algorithm, support vector machine, autocorrelation.

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.

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