doi:10.16186/j.cnki.1673-9787.2019120093

Received:2019/12/27

Revised:2020/04/13

Published:2021/03/15

Prediction method for attenuation decay of threaded connection incomplex working condition based on text data mining

WANG Changjian1,2, JIANG Guozhang1,2, DUAN Xianyin1,2

1.Key Laboratory of Metallurgical Equipment and Control of Ministry of Education， Wuhan University of Science and Technology， Wuhan  430081 ， Hubei， China;2.Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering， Wuhan University of Science and Technology， Wuhan  430081 ， Hubei， China

Abstract:In view of the easy failure of threaded connections under complex working conditions such as shock， vibration， or variable load environments， the traditional methods based on experiments and simulations cannot efficiently，economically and accurately identify the main causes and predict the attenuation. In this paper， using the advantages of artificial intelligence technology in the field of fault prediction， a data mining-based method for predicting threaded connection torque attenuation under complex working conditions was proposed. This method could mine the accurate mapping relationship between the threaded connection failure and the original data， and accurately find out the main reason of the threaded connection failure and predict the torque attenuation. Firstty， considering the special language structure that causes the polarity of the text to change， a regularized fault quantification method was proposed； Secondly， combined with the threaded connection torque attenuation professional field dictionary， the threaded connection torque attenuation was fault quantitatively rated based on the text description ； Then， the feature construction method based on the Logistic function model is proposed and the torque attenuation related feature set was constructed； Finally， a stacking integrated learning prediction model based on random forest and ridge regression algorithm was established. The prediction of the torque attenuation of the thrust rod bolt of a heavy truck was used as a practical case to verify the feasibility and effectiveness of the method. The integrated model prediction accuracy was improved by an average of 53. 39% compared with a single random forest and ridge regression algorithm model.

Key words:text data mining;threaded connection;torque attenuation;fault prediction;Stacking ensemble learning

 基于文本数据挖掘的复杂工况螺纹连接力矩衰减预测方法_王昌健.pdf