Dynamic weight-based learning method for data detection in manufacturing

Abstract

In this study, a dynamic weight-based method combined with principal component analysis (PCA) was developed for the first time for detecting measurement data in manufacturing. This weight-based learning technique can learn and train the measurement data sequence to isolate incorrect data sources for achieving high accuracy when detecting various types of data. Research has revealed that unsuitable image or data features might cause poor performance in industrial inspections. In contrast to the previous inspection methods, the weight-based learning method proposed in this study employs a dynamic learning algorithm for effectively and adaptively selecting optimal principle components to the support vector machine (SVM) algorithm and then establishes indicators. Finally, these PCA-based indicators act as substitutes for massive amounts of data in data processing and can be applied to timely detect data when the data contain redundant and incorrect inputs in a sequence. The experimental results indicate that the proposed method, which combines dynamic weight-based feature extraction with PCA, can provide useful indicators for detecting various types of manufacturing data and exhibited satisfactory performance in the data detection.

Dynamic ST-based PCA method for adaptive data detection

“…The hold-out procedure is employed by the SVM to determine the combination 24 : parameter C and radial basis function kernel parameter γ. The grid-search strategy is used to determine the two parameters in SVM.…”

“…The hold-out procedure is employed by the SVM to determine the combination 24 : parameter C and radial basis function kernel parameter g. The grid-search strategy is used to determine the two parameters in SVM. Wang et al 25 suggested trying exponentially growing sequencies of C (2 25 , 2 23 , ., 2 15 ) and g (2 215 , 2 213 , ., 2 3 ) to identify good input parameters when the grid-search method is adopted.…”