Features Fusion Exaction and KELM With Modified Grey Wolf Optimizer for Mixture Control Chart Patterns Recognition

Abstract

Control charts are significant diagnostic tools to detect and identify the quality fluctuation of the complex industrial process. In the practical production process, attention is being paid to the monitoring of mixture control charts, which usually coupled by two or more basic control charts modes. This research is to present a hybrid pattern recognition method for mixture control charts. The proposed method mainly covers the feature fusion extraction (FFE) and kernel extreme learning machine (KELM) with modified grey wolf optimizer (MGWO). The FFE module applies the original data and their shape and statistical features as the features, then uses kernel entropy component analysis to reduce the feature dimension and extract valid features. One significant difficulty of KELM is to get suitable parameters like the penalty parameter and the kernel function parameter value. MGWO is established to the optimal tuning of KELM parameters, which improves the population initialization and nonlinear convergence factor of traditional grey wolf optimizer. The proposed methodology is promising to obtain a better classification recognition rate, less computational time and achieves more stable results in the pattern recognition problem of mixture control charts. INDEX TERMS Control chart patterns recognition, feature fusion extraction, kernel extreme learning machine, kernel entropy component analysis, modified grey wolf optimizer.

Orca-RBFNN: A New Machine Learning Method for Control Chart Pattern Recognition

“…Due to the importance of the issue, in recent years, extensive studies have been conducted in this regard and various methods have been proposed to control the production process. Most of the introduced methods have used machine learning (ML) algorithms such as multi-layer Perceptron neural network (MLPNN), support vector machine, adaptive neuro-fuzzy system (ANFIS), probabilistic neural network (PNN) [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22].…”

Control Charts and Machine Learning for Anomaly Detection in Manufacturing

“…They showed that the proposed method can significantly improve the recognition accuracy and the recognition rate and the run time of CCPR as well as deliver satisfying prediction results even with relatively small-sized training samples. Another CCPR technique based on the feature fusion approach is presented in Zhang et al 145 . In conclusion, developing new and refining existing ML-based control charts and ML-based CCPR models, as well as interpreting out-of-control techniques based on data fusion and feature fusion methods are good directions for future research of the scientist in the field of this chapter (Weese et al 34 ).…”

Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective

“…They showed that the proposed method can significantly improve the recognition accuracy and the recognition rate and the run time of CCPR as well as deliver satisfying prediction results even with relatively small-sized training samples. Another CCPR technique based on the feature fusion approach is presented in Zhang et al 145 . In conclusion, developing new and refining existing ML-based control charts and ML-based CCPR models, as well as interpreting out-of-control techniques based on data fusion and feature fusion methods are good directions for future research of the scientist in the field of this chapter (Weese et al 34 ).…”