Informative singular value decomposition and its application in fault detection of planetary gearbox

The fault features of planetary gearboxes are modulated complexly and are submerged by other signal components, for its vibration signal has the characteristics of multi-source and multi transmission path. A fault detection method of planetary gearboxes based on informative singular value decomposition and envelope spectrum analysis (ISVD-ESA) is proposed in this paper. In this method, the advantage of blind source separation of singular value decomposition (SVD) method is combined with the ability of negentropy and cyclic autocorrelation (CA) in non-Gaussian characteristics recognition. The fast SVD is firstly performed to decompose the vibration signal into a series of singular value decomposition component signals (SVCSs). Secondly, the detector of negentropy combined with CA is applied to estimate the fault informativeness of each SVCS. The SVCSs are amplified by the fault informativeness and reconstructed to the out signal of ISVD. Finally, the fault features can be extracted by the ESA from the output signal of ISVD. The performance of the proposed method is verified by simulation and experimental studies. Results show that the proposed ISVD-ESA strategy can enhance the weak features of multi-modulation and accurately extract the faults of tooth tip pitting and misalignment of sun gear of the planetary gearbox.

Automated summary

This paper proposes a fault detection method for planetary gearboxes based on informative singular value decomposition and envelope spectrum analysis. The method decomposes the vibration signal into component signals using singular value decomposition, and estimates the fault informativeness using negentropy and cyclic autocorrelation. Finally, the fault features are extracted using envelope spectrum analysis.