Nonlinear mechanical response analysis and convolutional neural network enabled diagnosis of single-span rotor bearing system

• Published in: Scientific reports Vol. 14; no. 1; p. 10321
• Main Authors: Qian, Bing, Cai, Yinhui, Ran, Yinkang, Sun, Weipeng
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 06.05.2024; Nature Publishing Group UK; Nature Portfolio
• Subjects: Aviation; Classification; Convolutional neural network machine learning; Deformation; Faults; Machinery; Neural networks; Response analysis; Single-span rotor bearing system; Spindle bending; Spindle crack; Working conditions; Convolutional neural network machine learning; Spindle bending; Single-span rotor bearing system; Spindle crack
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-61180-6

The wide application of rotating machinery has boosted the development of electricity and aviation, however, long-term operation can lead to a variety of faults. The use of different measures to deal with corresponding malfunctions is the key to generating benefits, so it is significant to carry out the fault diagnosis of rotating machinery. In this work, a test bench for single-span rotor bearings was established, three faults, including spindle bending, spindle crack without end loading and spindle crack with end loading, are experimental analyzed with basic mechanical response. Moreover, a diagnosis is performed using a convolutional neural network, according to the differences in mechanical responses of the three faults obtained from experiments. For three faults, the change in the properties of spindle itself results in different axis trajectories and spectra. Compared with spindle bending fault, spindle crack fault not only cause 1×, 2×, 3× frequency component excitation, also 4×, 5× frequency component excitation. Additionally, the classification accuracy of the training set and the test set under machine learning for the three types of working conditions is 100%. This indicates that the network can significantly identify signal features so as to make effective fault classification.