《机电工程》杂志社科技期刊核心期刊论文投稿机电一体化机械、仪表电工自动化、计算机浙江大学主办

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Fault diagnosis of wind turbine bearing and gearbox based on group-sequence and multi-branch CNN-LSTM model

Published:2022-10-20 author:XU Qi-fa, CHENG Qi-liang, JIANG Cui-xia, et al. Browse: 541 Check PDF documents

Fault diagnosis of wind turbine bearing and gearbox based on

group-sequence and multi-branch CNN-LSTM model

XU Qi-fa 1,2, CHENG Qi-liang 1,2, JIANG Cui-xia1,2, WANG Xiang-xiang1

(1.School of Management, Hefei University of Technology, Hefei 230009, China; 2.Key Laboratory of Process

Optimization and Intelligent Decision-Making, Ministry of Education, Hefei University of Technology, Hefei 230009, China)

Abstract: In real industrial environments, the acquisition time of single-point data was usually several seconds or even shorter. Based on single-point data, the existing intelligent diagnosis algorithms for wind turbines were difficult to obtain satisfactory results. To this end, a group-sequence multi-branch convolution neural networks-long short term memory(CNN-LSTM) model(Gsmbclam model) with attention mechanism was proposed. Firstly, continuous multi-point data with the same sampling interval were formed into a group sequence sample, and group sequence waveforms and spectrums were input into two different one-dimensional convolution neural network(1D-CNN)s for adaptive feature extraction. Secondly, the extracted features and manually features were weighted through the attention mechanism, and the weighted features were fused and input into LSTM. Then, considering the different misclassification costs of different categories in fault classification, focal loss function was used to replace the traditional cross entropy loss function. Finally, the diagnosis results were output by the SoftMax classifier. Comparison tests are performed using a real data set containing 54,000 raw waveforms, spectra, and manually features, which comprise five different types of bearing and gear faults and one type of normal data.The results show that the accuracy, precision, recall and F1score of the proposed method reach 98.40%, 98.46%, 98.63% and 98.30%, respectively. Its performance is better than the model based on single point data or single branch. Each index has obvious advantages compared with other competing deep learning models. The introduction of focus loss function solves the problem of different classification costs in fault diagnosis.

Key words: bearingsfault diagnosis; gearboxesfault diagnosis; group sequence; multi-branch; convolution neural networks-long short term memory(CNN-LSTM); Focal loss

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