A novel co-modulation and hybrid resolution strategy (CHRS) for fault diagnosis of planetary gearboxes

Abstract

Planetary gearboxes (PGs) serve as vital transmission links in rotating machinery, and diagnosing faults within them is crucial for effective maintenance. Traditional deep learning methods often operate as ”black boxes,” offering limited transparency in interpreting results, especially when analyzing the complex vibration signals of PGs. To address this issue, this paper proposes a co-modulation model combined with a hybrid resolution strategy (CHRS), leveraging amplitude modulation (AM) and frequency modulation (FM) intensities, to enhance the interpretability of fault diagnosis. First, a more comprehensive and adaptable expression of the co-modulation model is developed to describe gear faults. Second, CHRS links the model’s generated signal with the actual monitoring data, establishing an intrinsic connection between the mathematical model and the data. An updating mechanism based on partial differential analysis is established for model parameter estimation. A partial differential-based updating mechanism is employed for model parameter estimation, enabling the quantitative analysis of model coefficients (including AM and FM), even with a limited number of training samples. Finally, the support vector machine (SVM) is employed to train and test these model parameters, facilitating the identification of different fault types through experimental data, thus validating the effectiveness of CHRS. In summary, CHRS significantly improves the interpretability of PG fault diagnosis by enhancing both the modeling process and quantitative analysis of vibration signals.