Research on Fault Diagnosis Model of Convolutional Neural Network Based on Signal Decomposition

Abstract

The complex equipment such as aeroengines has a complicated internal structure. Due to long-term exposure to extremely harsh external environmental conditions such as temperature and pressure, aeroengines often have various forms of failure, which seriously affect the normal flight of the aircraft. It is difficult for traditional models to extract accurate fault information from complex vibration signals, which increases the difficulty of troubleshooting for aircraft engines. Aiming at this problem, a fault diagnosis model using the combination of variational mode decomposition and convolutional neural network is proposed. First, the original signal is decomposed by variational mode decomposition, and then the decomposed signal is reconstructed into a two-dimensional characteristic matrix. Finally, the reconstructed matrix is used as the input of the convolutional neural network to realize the classification of typical failure modes. Compared with the traditional method, this method can extract the internal fault characteristics of the vibration signal better, and the fault recognition accuracy rate is higher.