Open AccessResearch on Fault Diagnosis Method of Gearbox Based on SA and BP-AdaBoost
Author(s) -
Yangyang Zhang,
Yunyi Jia,
Weiyi Wu,
Xiaobo Su,
Dong Liang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/793/1/012009
Subject(s) - adaboost , sensitivity (control systems) , fault (geology) , pattern recognition (psychology) , artificial intelligence , artificial neural network , feature (linguistics) , computer science , feature selection , signal (programming language) , time domain , vibration , engineering , support vector machine , computer vision , electronic engineering , linguistics , philosophy , physics , seismology , programming language , geology , quantum mechanics
In order to solve the problems of difficult selection of state feature parameters and poor accuracy of single BP Neural Network in gearbox fault diagnosis, a gearbox fault diagnosis method based on SA and BP-AdaBoost is proposed. Taking the typical fault of a gearbox as the research object, the vibration signal of the typical working state of the gearbox is collected through the preset fault experiment. The time domain statistical parameters with high sensitivity is selected as the feature vectors by the Sensitivity Analysis method. Then these state feature vectors are input into BP-AdaBoost model for training and testing, and their results are compared with those of BPNN model. The results show that the proposed method can quickly and effectively diagnose the fault of gearboxe, and is better than BPNN model.