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

Min Zhang,Wenming Cheng,Haibo Wang,Guowen Xiong,Xunjie Zhang

doi:10.1109/access.2020.2976795

Min Zhang, Wenming Cheng + Show 3 more

Open Access

https://doi.org/10.1109/access.2020.2976795

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 12	License type: CC BY 4.0

Affiliation: Southwest Jiaotong University

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.

Highlights

With the development of data collection technology, control charts are the most popular tools in statistical process control and mainly apply to record or monitor the fluctuations in quality problems of complex industry
We propose many traditional approaches to verify the effectiveness of the proposed FFE_KELM_MGWO approach, like features, feature extraction methods, parameters optimization methods
For improving the recognition accuracy and search speed, this paper proposes a new convergence factor update formula based on hyperbolic tangent function

Summary

INTRODUCTION

With the development of data collection technology, control charts are the most popular tools in statistical process control and mainly apply to record or monitor the fluctuations in quality problems of complex industry. Data of most practical complex processes have the characteristics of timevarying, large amount and nonlinear To solve these problems, a feature fusion extraction (FFE) method is proposed. A modified grey wolf optimizer algorithm (MGWO), which improves the population initialization and nonlinear convergence factor, is applied to optimize the kernel function parameters of KELM. Experimental results indicate that the proposed approach can effectively recognize mixture control charts patterns and perform better. 2. A new nature-inspired method MGWO, which improves the population initialization and nonlinear convergence factor, is applied to optimize the KELM kernel function parameters. 3. The proposed FFE_KELM_MGWO methodology tends to obtain better patterns recognition rate, less computational time and achieves more stable results in the mixture CCPs problem when compared to several other methods.

THE PROPOSED FFE FEATURE EXTRACTION SCHEME

MODIFIED GREY WOLF OPTIMIZER FOR TWO-DIMENSIONAL KELM PARAMETERS

PERFORMANCE ANALYSES

PERFORMANCE OF FFE METHOD EVALUATION

PERFORMANCE OF RECOGNIZER IN THE STATISTICAL PROCESS CONTROL METHOD

PERFORMANCE OF MGWO OPTIMIZATION METHOD

CONCLUSION