Industrial Product Defect Analysis Based On Multi-Classifier Integration

The analysis of defects in the manufacture of industrial products is one of the important ways to improve the process of product manufacturing.It has important research significance and application value in terms of product quality and marketing revenue.With the rapid development of computer technology and the comprehensive deployment of automation systems,the difficulty of information collection and storage has been greatly reduced.Data with potential information and value are constantly accumulating.At the same time,machine learning,data mining and other methods in all walks of life made rapid progress and application.The use of these data,however,due to the nature of the industry,is far less than that of other industries.Thus,these data did not really play its proper value.Based on the characteristics of manufacturing data,this paper therefore summarizes the general process of the product defect analysis,the data processing method and the statistical analysis.We also turn the problem which analyzes the relationship between the quality inspection and the defects data of the product into another issue that is to establish a classification model between these two factors through a statistical learning method.However,there are many kind of defects in the data and each have imbalanced size of samples,which is a big obstacle to the establishment of the classification model in a general sense.In this paper,we propose a multi-classifier model which combines cost-sensitive and ensemble methods to solve the two obstacles simultaneously.The multi-classifier system is constructed by combining the re-weighting method with the cost of misclassification.The experiments show that the proposed model can effectively deal with the multi-classification problem of imbalanced classes,and can balance the cost of classification and the accuracy of prediction.In addition,fitting of the integrated decision trees can be used to measure the relevant importance of features,of which the highest can be thought as an factor to impact the final defect of product.