An Intelligent Fault Diagnosis Method of Variable Condition Gearbox Based on Improved DBN Combined With WPEE and MPE

Abstract

Gear transmission is one of the most commonly used transmission methods in mechanical equipment. By analyzing the vibration data of gearbox, an improved deep belief network (DBN) algorithm for gear fault diagnosis based on wavelet packet energy entropy (WPEE) and multiscale permutation entropy (MPE) is proposed. Firstly, the vibration data of gearbox with various fault types under multiple working conditions are collected. Secondly, the energy entropy of wavelet packet and the entropy distribution of multiscale permutation are calculated respectively to form a combined feature matrix. Then, the improved threshold adaptive DBN is used to further extract the fault signal features, and finally the deep layer features are classified. By analyzing the vibration data of multi-platform gearbox, a high and stable diagnostic accuracy is obtained.

Keywords

• Gear fault
• wavelet packet energy entropy
• multiscale permutation entropy
• deep belief network