Life Span Prediction of Key Fixtures Integrating Principal Component Analysis and Random Forest Model

doi:10.3969/j.issn.1007-7375.2023.05.017

Abstract

Abstract: To address the difficulties in predicting the remaining life span of key fixtures in assembly of initiating explosive product and providing reference for field data, a life span prediction model of key fixtures for initiating explosive product assembly is established integrating assembly feature extraction and minimization of impurity weighted sum. In addition, a prototype of a life span prediction system for key fixtures in initiating explosive product assembly is developed. In this paper, assembly features of key fixtures are extracted by principal component analysis, while the remaining life span of key fixtures is predicted by random forest model. Comparative analysis shows that, compared with traditional methods, the prediction accuracy of the proposed method is increased by 0.89%, and the mean square error, root mean square error and prediction time are decreased by 22.3%, 22.3% and 24.8%, respectively. It is proved that the method has the advantages of high prediction accuracy, good stability and fast prediction speed.

Key words: initiating explosive product, key fixture, principal component analysis, random forest, remaining life span, life span prediction

CLC Number:

TP391

ZHU Xiaofeng, XU Manfei, LIU Zhihong, LENG Jiewu. Life Span Prediction of Key Fixtures Integrating Principal Component Analysis and Random Forest Model[J]. Industrial Engineering Journal, 2023, 26(5): 149-158.

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