Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function

Abstract

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 B Pingyu Jiang 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.

Using Machine Learning Classifiers to Recognize the Mixture Control Chart Patterns for a Multiple-Input Multiple-Output Process

“…In this study, an MIMO system is assumed to be disturbed by four types of disturbances characterized by Equations (10)- (13). This study used ANN, SM, ELM and MARS to recognize the mixture CCPs in an MIMO system.…”

“…This study assumes that an MIMO process is disturbed by four single disturbances, which are described by Equations (10)- (13). When any one of those four disturbances (i.e., Equations (10)-(13)) is introduced in the MIMO system, this study refers to that situation as the presence of a single CCP.…”

“…In addition to the use of ANNs, support vector machine (SVM) is another commonly used classifier for CCP recognition. A method combining fuzzy SVM, a hybrid kernel function, and a genetic algorithm (GA) was designed for CCP recognition [13]. The results showed that the fuzzy SVM-based classifier outperformed the methods of a learning vector quantization network, multilayer perceptron network, and probability neural network.…”

Statistical Process Control with Intelligence Based on the Deep Learning Model

“…Since 1920s, statistical process control (SPC) theory has played an important role in product quality improvement and quality supervision [1]. SPC mainly uses a statistical analysis method to monitor the production process, and scientifically distinguishes the random fluctuation and abnormal fluctuation of product quality in the production process [2,3].…”

One‐sided control chart based on support vector machines with differential evolution algorithm

“…Zhang et al developed a general monitoring framework for detecting location shifts in complex processes using SVM model and multivariate EWMA chart. Zhou et al investigated a method of control chart pattern recognition by integrating fuzzy SVMs with hybrid kernel function and genetic algorithm. Lee and Kim presented a time‐adaptive SVDD‐based control chart that can address not only non‐normal observations but also time‐varying processes.…”