Composite fault diagnosis for planetary gearboxes: Application of fault feature extraction and multi-channel fusion methods

The fault features in the vibration signals of planetary gearboxes exhibit strong coupling and nonlinearity, significantly increasing the complexity of fault feature extraction and diagnosis. To address these issues, this work proposes a composite fault diagnosis method based on multi-parameter multi-channel fusion (MP-MCF). This method integrates fundamental characteristics that show distinct evolutionary trends as fault severity increases to create a new integrated fault feature metric called sample entropy root mean square (SE-Rms). By effectively improving the discriminatory quality of different fault properties, this metric makes it possible to identify composite defects with more reliability. Additionally, based on SE-Rms, a multi-parameter matrix is created and fed into a support vector machine (SVM). Concurrently, an comprehensive MP-MCF diagnostic framework is formed by introducing the multi-channel evidence fusion theory to re-diagnose misdiagnosed samples. The results of the experiment show that this method works effectively for diagnosing composite faults in planetary gearboxes with different structures. The diagnostic accuracy reached 96.6% after multi-channel fusion, confirming the efficacy of the proposed MP-MCF method and SE-Rms feature and offering useful guidance for the diagnosis of composite faults in gearbox systems.