Year: 2025
Type: article
Abstract: As some of the high-frequency faults in rotating machinery, the early detection and accurate identification of rub-impact faults have attracted much attention. Due to the low vibration property and strong environment noise, it is still a challenge problem to deal with the early diagnosis of rub-impact faults. In this paper, a new rub-impact fault diagnosis method is proposed which consists four phases: the decomposition phase, the dynamic feature extraction phase, the diagnosis model construction phase, and the verification phase. In the first phase, the original vibration signals are decomposed based on empirical mode decomposition (EMD), and the most correlated components are selected using the Pearson correlation coefficient (PCC). Then, in the second phase, the fault dynamics of the selected components are identified based on dynamic learning, which means they are more sensitive to the small faults in the initial stage. In the last phase, a new fault feature deep fusion module is constructed based on the identified dynamics information and integrate into the convolution neural networks to derive the diagnosis model (dynamic CNN). Finally, the misjudgment self-correction mechanism is implemented based on the comparison of fault dynamics in the verification phase. Compared with traditional methods, the experiment results illustrate that the proposed method can detect the rub-impact faults more effectively.
Source: Mechanical sciences
Authors: Jinmin Shi, Ruixiao Zhang, Qian Wang
Institutions: CHN Energy (China), Zhengzhou University of Light Industry
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $765
Award: G839274009
OpenAlex ID: W4415384158