A Novel Fault Identification Method Using Modified Morphological Denoising via Structuring Element Optimization for Transmission Systems of Shipborne Antennas

Abstract

Unlike common rotating machines, shipborne antennas always work under variable loads and suffer from extreme ocean conditions, which makes monitoring their condition and early fault identification necessary and challenging. However, extracting weak fault characteristics from vibration signals accurately and efficiently is difficult because of multi-modulation phenomenon and heavy noise. Therefore, an adaptive denoising method based on morphological filtering via structuring element optimization is proposed in this paper. The proposed method mainly includes two aspects: an adaptive spectrum segmentation algorithm via scale expression and a criterion based on the characteristic energy ratio for structuring element optimization. Experimental signals and a set of comparisons verify the effectiveness and robustness of the proposed method. The proposed method is also applied to identify an early antenna drivetrain fault in a real case, showing its superiority and effectiveness.