Precision reducers used in aerospace servo mechanisms often have the characteristics of high accuracy and strong bearing capacity due to the particularity of their working environment. Transmission error, as an important parameter affecting the accuracy of precision reducer, is different from ordinary reducers, and often requires that its maximum transmission error can be effectively controlled within 3’. In order to efficiently test the accuracy of the reducer, this paper designed a set of test devices for the accuracy measurement of the reducer of the aerospace servo mechanism, and now conducts a systematic error analysis of the overall test device, determines the error range, and finally, the uncertainty is integrated to confirm that the error meets the design requirements.
To solve the problem of noise interference and difficulty in feature extraction of vibration signals of planetary gearbox, propose a fault diagnosis method of planetary gearbox based on improved sparrow search algorithm optimized variational mode extraction (ISSA-VME) and multi-scale fuzzy entropy (MFE). The vibration signal was decomposed by ISSA-VME, and the multi-scale fuzzy entropy of the original signal was calculated according to the IMF component. The fault feature set composed of MFE was applied to the SVM classifier optimized by the improved sparrow search algorithm for fault pattern recognition. The experimental results demonstrate that compared with ICEEMDAN and VMD methods, ISSA-VME has a better effect and higher efficiency on vibration signal decomposition. The identification rate of missing teeth, cracks, wear, and other faults of planetary gearbox sun gear by the proposed method reaches 97.50%.
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