Intelligent Fault Diagnosis of Gearbox Based on Vibration and Current Signals: A Multimodal Deep Learning Approach

• Published in: 2019 Prognostics and System Health Management Conference (PHM-Qingdao) pp. 1 - 6
• Main Authors: Jiang, Guoqian, Zhao, Jingyi, Jia, Chenling, He, Qun, Xie, Ping, Meng, Zong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2019
• Subjects: Deep learning; Fault diagnosis; Feature extraction; gearbox; Generators; information fusion; multimodal deep learning; Neurons; Vibrations; Wind turbines
• DOI: 10.1109/PHM-Qingdao46334.2019.8942903

This paper proposes a new intelligent fault diagnosis approach based on multimodal deep learning to fuse vibration and current signals to diagnose wind turbine gearbox faults. The proposed method typically consists of modality-specific feature learning network and feature fusion network, specifically based on a popular deep learning model named deep belief networks (DBNs). First, two individual DBNs are designed to learn fault-related features directly from raw vibration signals and current signals, respectively. Then, the learned vibration-based features and current-based features are further fused by a third DBN to output the final diagnosis results. The proposed approach is verified on a wind turbine drivetrain gearbox test rig. The experimental results demonstrate that the proposed approach outperformed the compared methods based on single sensor and data-level fusion in terms of diagnostic accuracy, which attributes to the complementary diagnosis information from vibration signals and current signals.