Abstract
A rolling bearing is an important part of rotating machinery, and it is widely used in the petrochemical industry, aerospace industry and other industries. Hence, it is of great significance to carry out condition monitoring and fault alarms for rolling bearings. Aiming at the problem of the rolling bearing fault, a method of an improved deep convolutional denoising auto encoder abnormal feature extraction and the Kullback-Leibler divergence threshold alarm is proposed. The experiment verification is carried out on the rotor bearing experiment platform. The experiment results show that the proposed method has good denoising performance and micro fault feature extraction ability under the condition of no fault data training and no frequency domain transformation. High accuracy, good efficiency and strong robustness of the proposed method for an early fault alarm are demonstrated by the experiment as well.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Journal of Acoustics and Vibration
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
