A novel signature extracting approach for inductive oil debris sensors based on symplectic geometry mode decomposition

On line lubricating oil debris measurement is an efficient way to judge the operating status of a machine, and the inductive oil debris sensor is widely adopted. Considering the debris sensor works in a harsh environment, it is a challenge work to reduce or separate the noise via signal processing, especially for the requirements of detecting small-sized wear debris. To overcome the limitations of existing methods, a novel signal decomposition method called symplectic geometry mode decomposition (SGMD) is proposed to extract the signature of oil debris. SGMD can remodel the state and eliminating noise adaptively, and the simulation results manifest that SGMD can extract the signature of debris accurately and effectively. When analyzing the experimental signal, the SGMD and EMD are combined, and the results show that it has a better decomposition ability than EMD or wavelet decomposition.

Automated summary

The article introduces a novel signal decomposition method called SGMD for extracting the debris signature, showing its accurate and effective performance. Experimental results demonstrate that combining SGMD and EMD has better decomposition ability than EMD or wavelet decomposition.