Multiscale dynamically parallel shrinkage network for fault diagnosis of aviation hydraulic pump and its generalizable applications

• Published in: ISA transactions 2024-11, Vol.154, p.57-72
• Main Authors: Fu, Song, Lin, Lin, Wang, Yue, Zhao, Minghang, Guo, Feng, Zhong, Baihong, Zhong, Shisheng
• Format: Article
• Language: English
• Subjects: Aviation hydraulic pumps; Deep learning; Fault diagnosis; Multiscale learning; Soft thresholding
• ISSN: 0019-0578; 1879-2022; 1879-2022
• DOI: 10.1016/j.isatra.2024.08.009

Aiming to address the multiscale characteristics and noise corruption problems in the vibration signals of aviation hydraulic pumps, this article develops a novel Multiscale Dynamically Parallel Shrinkage Network (MDPSN) to learn complementary and rich fault-related multiscale features, with the ultimate goal of yielding higher diagnostic accuracy. One significant property is the development of a novel dynamically parallel shrinking module (DPSM) that adaptively generates independent soft thresholds for different scales, effectively shrinking noise-related features to zeros. On one hand, DPSM aggregates and interacts with features at all scales to construct a global feature representation containing richer fault-related information, which is served as the foundation for soft thresholding generation, significantly improving the accuracy and rationality of the generated thresholds. On the other hand, DPSM can adaptively generate individual soft threshold for each scale, allowing each scale to use an independent threshold tailored to its own characteristic to eliminate noise-related information. This avoids the issues of over-denoising or under-denoising caused by the uniform application of thresholds across all scales. Finally, the effectiveness of MDPSN is validated by a series of experiment comparisons on an aviation hydraulic pump dataset and two bearing datasets with various types of noise. The experimental results demonstrate that MDPSN achieves superior diagnostic accuracy compared to five other comparison methods. •A novel DPSM is designed to adaptively generate soft thresholds for all different branches in parallel.•A novel MDPSN is developed for achieving accurate fault diagnosis under high noisy environment.•MDPSN can learning complementary and rich diagnostic information at different scales.•MDPSN automatically eliminates the noise-related information in multiscale features learning