As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
To address the problem that traditional bearing fault diagnosis methods rely on professional knowledge and are tedious, this paper proposes an end-to-end CNN-based bearing fault diagnosis model to achieve automatic fault recognition. In addition, considering the problem that noise exists in the actual working conditions, a bearing fault diagnosis model based on Auto-encoder (AE) combined with CNN is proposed (AE-CNN). The noisy signal is coded and decoded by the designed AE, and the de-noised result is used as the input of the designed CNN to achieve the bearing fault diagnosis under noisy conditions. Experiments on CWRU have proved the effectiveness of the designed CNN and AE-CNN. The designed CNN achieves 99.83% fault diagnosis accuracy under noise-free condition. The AE-CNN achieves 97.14% fault diagnosis accuracy under –4db signal-to-noise ratio (SNR) noise condition, which is 2.31% higher than the CNN with the same noise, and compared with the results of other advanced methods, it has achieved competitive results.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.