TL;DR
The proposed MSD-DCSFNet framework maintains over 90% diagnostic accuracy even under a 0 dB signal-to-noise ratio combined with a 60% label noise rate, and these results validate its practical value for diagnosis using imperfect industrial data.
Rolling bearing fault diagnosis under strong noise remains a challenging problem because defect-induced vibration responses are typically non-stationary, weakly impulsive, and easily submerged by background interference. In addition, fault categories with different damage severities often exhibit similar dominant perio...
As critical components of mechanical equipment, the health condition of bearings directly determines the stable operation and operational safety of machinery. Thus, timely early fault diagnosis of bearings is of great practical significance. Nevertheless, collected vibration signals are frequently contaminated by str...
The results show that SAMACNN outperforms both classical and advanced methods on the two datasets, demonstrating strong robustness and generalization capability in complex variable-condition measurement environments.
Multiscale fusion-based fault diagnosis methods can exploit complementary fault information across different scales and are therefore effective in improving diagnostic accuracy. However, when noisy multiscale information is represented or fused, noise components from different scales tend to interfere with one another,...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
