Abstract
Under complex working conditions with noise interference, the fault feature of planetary gearbox is difficult to be extracted and the fault mode is difficult to be identified. To tackle this problem, the technologies of variable multi-scale morphological filtering (VMSMF) and average multi-scale double symbolic dynamic entropy (AMDSDE) are proposed in this paper. VMSMF selects Chebyshev Window as the structural element and automatically selects the optimal-scale parameters according to the signal characteristics of the planetary gearbox, which improves the filtering accuracy and calculation efficiency. AMDSDE fully considers the correlation between various state modes. Once combined with relevant knowledge of Mathematical statistics, the algorithm can effectively reduce misjudgment. Firstly, the turn domain resampling (TDR) is used to transform the time domain signal of variable speed into the angle domain signal that is not affected by speed change. Secondly, the proposed VMSMF is used to de-noise the vibration signal, and the fault signal with a high signal-to-noise ratio is obtained. Finally, AMDSDE is used to extract the entropy value of the fault signal and judge the fault type. The proposed technology is verified by four kinds of signals collected from the sun gear of the planetary gearbox under non-stationary working conditions.
Highlights
Scholars have successfully carried out a series of research on the filter scale and structural element selection
5.1 Conclusions Based on the traditional multi-scale morphological filtering the Variable Multi-scale Morphological Filtering (VMSMF) is proposed in this paper
An Average Multi-scale Double Symbolic Dynamic Entropy (AMDSDE) method based on Multi-scale Symbolic Dynamic Entropy (MSDE) is proposed to address the difficulty in accurate distinguishing the faults of the sun gear by only depending on frequency
Summary
2.1 Mathematical Morphological Algorithm 2.11 Structural Elements In mathematical morphology, structural elements are reference objects with special shapes, which are subjected to the influence of the morphological characteristics of original signals directly. The scale of non-flat structural elements is determined based on the characteristics of the signal, and the morphological calculation is carried out by addition and subtraction methods. The Chebyshev window is selected as the structural element of morphological algorithm. It can be seen from table 1 that using Chebyshev window as the structural element of morphological filtering, the stability of vibration signal after filtering is the best. This paper selects Chebyshev window function as the structural element of morphological filtering. Because structural elements need to be used to strike a balance between noise removal and effective information suppression, signal characteristics need to be used to determine the structural element parameters of morphological filter.
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