Research of Fault Feature Extraction and Diagnosis of Planetary Gear Train based on SPS and CNN

Abstract

In order to solve the problem that the complex structure and operating conditions of planetary gear box lead to the difficulty of signal fault feature extraction， the frequency spectrum feature of gearbox vibration signal with faults is preliminarily deduced by analyzing the vibration mechanism of planetary gear train. The method of harmonic product spectrum （HPS） and sideband product spectrum （SPS） is used to accurately extract the fault characteristic frequencies of the simulation signals under the condition that the noise interference and fault impact are not obvious. The vibration signals of the planetary gearbox under different operating conditions and different fault states are further collected，and the extracted fault features are input into the convolutional neural network for fault identification. The fault information of the gearbox is obtained successfully，which proves the feasibility of the proposed method in fault diagnosis of the planetary gearbox.

Keywords

• Planetary gear train;Fault feature extraction;Harmonic product spectrum;Sideband product spectrum;Convolutional neural network