Abstract
The gearbox is an important component in the mechanical transmission system and plays a key role in aerospace, wind power and other fields. Gear failure is one of the main causes of gearbox failure, and therefore it is very important to accurately diagnose the type of gear failure under different operating conditions. Aiming at the problem that it is difficult to effectively identify the fault types of gears using traditional methods under complex and changeable working conditions, a fault diagnosis method based on multi-sensor information fusion and Visual Geometry Group (VGG) is proposed. First, the power spectral density is calculated with the raw frequency domain signal collected by multiple sensors before being transformed into a power spectral density energy map after information fusion. Second, the obtained energy map is combined with VGG to obtain the fault diagnosis model of the gear. Finally, two datasets are used to verify the effectiveness and generalization ability of the method. The experimental results show that the accuracy of the method can reach 100% at most on both datasets.
Funder
Shandong Province Higher Educational Science and Technology Program
Shandong Province Innovation and entrepreneurship training program for college students
Research and application of key technologies of cargo distribution and transportation based on BDS and 5G
Subject
General Physics and Astronomy
Reference37 articles.
1. Babu, T.N., Ali, P.S.N., Prabha, D.R., Mohammed, V.N., Wahab, R.S., and Vijayalakshmi, S. (2022). Fault Diagnosis in Bevel Gearbox Using Coiflet Wavelet and Fault Classification Based on ANN Including DNN. Arab. J. Sci. Eng., 1–27.
2. Fault diagnosis of planetary gearbox using multi-criteria feature selection and heterogeneous ensemble learning classification;Measurement,2020
3. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals;Mech. Syst. Signal Process.,2016
4. Deep Learning Based Intelligent Industrial Fault Diagnosis Model;Comput. Mater. Contin.,2022
5. A gear fault diagnosis method based on improved accommodative random weighting algorithm and BB-1D-TP;Measurement,2022
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献