Marine fire pump motor bearings fault feature enhancement and diagnosis based on adaptive SSA and improved TEO

Objectives The marine fire pump motor bearings working environment is complex, and the accuracy of fault diagnosis being low. A fault feature enhancement and diagnosis method for marine fire pump motor bearings based on adaptive steady-state subspace analysis (SSA) and improved Teager energy operator (TEO) is proposed to address this issue.

Methods Firstly, optimize the traditional SSA algorithm to adaptively determine the dimensionality of the Hankel matrix by false nearest neighbor method, and extract non-stationary signals which with the best fault features from the vibration signal through kurtosis. Secondly, by improving the TEO algorithm, the proportion of fault feature information in vibration signals is increased, fault features are enhanced and fault are diagnosed. Finally, the effectiveness of the proposed method is verified through simulation experiments and engineering experiments.

Results The experimental results show that the method can accurately distinguish the fault characteristic frequency and its harmonics of bearings and can accurately diagnosing bearing faults.

Conclusions The research results can provide reference for the fault diagnosis of marine pump motor bearings.