Fault diagnosis of electric drill winch gearbox based on LSTM-RF

被引：0

作者：

Liu, Guangxing ^{[1
,2
]}

Ma, Yihao ^{[1
]}

机构：

[1] School of Electronic Engineering, Xi'an Shiyou University, Xi'an

[2] Key Laboratory of Oil and Gas Well Measurement and Control Technology, Xi'an

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2024年 / 43卷 / 21期

关键词：

electric drilling winch; fault diagnosis; gearbox; long short-term memory (LSTM); random forest (RF) algorithm;

D O I：

10.13465/j.cnki.jvs.2024.21.017

中图分类号：

学科分类号：

摘要：

Here, to improve the correctness and efficiency of fault diagnosis for winch gearbox of petroleum electric drill winch, a fusion model based on long short-term memory (LSTM) and random forest (RF) was proposed. Firstly, LSTM could be used to learn complex features from large-scale data, these features were taken as inputs to RF. Then, non-linear and high-dimensional data were processed with RF, and features were classified to realize recognition of different fault states of gears. Finally, a comprehensive dataset containing multiple types of gear faults was established using real-time data in Operation process of electric drill winch gearbox. The experimental results showed that the correct rate of LSTM gear fault diagnosis is 94. 67%, RF gear fault diagnosis correct rate is 94. 34%, support vector machine (SVM) gear fault diagnosis correct rate is 82.00%, K-nearest neighbor (KNN) gear fault diagnosis correct rate is 88. 33%, while the correct rate of the proposed LSTM-RF fusion model gear fault diagnosis reaches 98. 33% to overcome the limitation of a single model and improve diagnosis accuracy. The study showed that LSTM-RF fusion model has a better fault diagnosis capability for winch gearbox of electric drill winch. © 2024 Chinese Vibration Engineering Society. All rights reserved.

引用

页码：156 / 162and230

共 17 条

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