| Citation: | SUN H Y，YANG Z P，WANG Y W，et al. Compound fault diagnosis of planetary gearbox based on RSSD-CYCBD by adaptive parameter optimization[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（10）：3139-3150 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0773
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The coupling of multiple vibration sources of planetary gearboxes results in difficulty in identifying fault sources, and weak fault features are easily masked by noise and strong fault features. In addition, signal attenuation caused by the propagation path causes weak fault features. To address these issues, a multi-fault coupled signal separation and diagnosis method for planetary gearboxes utilizing resonance-based sparse signal decomposition (RSSD) by adaptive parameter optimization and maximum second order cyclostationary blind deconvolution (CYCBD) was proposed. According to the different resonance properties of bearing faults and gear faults, the multi-fault coupled signal was divided into high resonance components containing gear fault features and low resonance components mainly containing bearing fault features by RSSD. Then, the two components were treated by the CYCBD to eliminate the influence of the propagation path and noise interference, so as to enhance and extract weak fault features. In particular, to solve the problems of difficulty in parameter optimization, dependence on artificial experience, and poor adaptation in RSSD and CYCBD, an adaptive parameter optimization method based on the squirrel search algorithm (SSA) was proposed, and a composite index integrating kurtosis of envelope spectrum, root mean square of autocorrelation function maximum, and characteristic frequency ratio was designed as an optimization objective. Finally, envelope demodulation was performed on the deconvolved signal to extract the fault feature frequency and identify different fault sources. The effectiveness and feasibility of the proposed algorithm were verified by the multi-fault simulation signal and the measured signal of the planetary gearbox. Moreover, the proposed method was integrated into edge computing equipment to provide solutions for state detection and diagnosis, as well as remote operation and maintenance of rotating machinery such as planetary gearboxes.
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