Open AccessGear Fault Detection in a Planetary Gearbox Using Deep Belief Network
Author(s) -
Hu Hao,
Fuzhou Feng,
Jiang Feng,
Zhou Xun,
Junzhen Zhu,
Xue Jun,
Pengcheng Jiang,
Li Yazhi,
Qian Yongchan,
Sun Guanghui,
Chen Caishen
Publication year - 2022
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2022/9908074
Subject(s) - prognostics , deep belief network , fault (geology) , feature extraction , vibration , signal (programming language) , engineering , time domain , pattern recognition (psychology) , feature (linguistics) , artificial intelligence , set (abstract data type) , frequency domain , fault detection and isolation , computer science , deep learning , computer vision , reliability engineering , acoustics , linguistics , philosophy , physics , actuator , seismology , programming language , geology
Traditional prognostics and health management (PHM) methods for fault detection require complex signal processing and manual fault feature extraction, and the accuracy is low. To address these problems, a fault diagnosis method of planetary gearbox based on deep belief networks (DBNs) is proposed. Firstly, the vibration signals of the planetary gearbox are collected and analyzed in the time domain and the frequency domain. Then, the DBN model and optimal parameters are determined to meet the task requirements. Finally, the vibration data is divided into training set and test set and input into the DBN model, which can realize the automatic feature extraction and fault recognition of vibration signals. The results show that the identification accuracy reaches 97% under five working conditions of planetary gearbox.