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

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.