Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks

被引:1045
作者
Ince, Turker [1 ]
Kiranyaz, Serkan [2 ]
Eren, Levent [1 ]
Askar, Murat [1 ]
Gabbouj, Moncef [3 ]
机构
[1] Izmir Univ Econ, Elect & Elect Engn Dept, TR-35330 Izmir, Turkey
[2] Qatar Univ, Dept Elect Engn, Doha 2713, Qatar
[3] Tampere Univ Technol, Dept Signal Proc, Tampere 33720, Finland
关键词
Convolutional neural networks (CNNs); motor current signature analysis (MCSA); BEARING DAMAGE DETECTION; DIAGNOSIS; SIGNAL; DECOMPOSITION; SENSORLESS; MODEL;
D O I
10.1109/TIE.2016.2582729
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such fixed and hand-crafted features may be a suboptimal choice and require a significant computational cost that will prevent their usage for real-time applications. In this paper, we propose a fast and accurate motor condition monitoring and early fault-detection system using 1-D convolutional neural networks that has an inherent adaptive design to fuse the feature extraction and classification phases of the motor fault detection into a single learning body. The proposed approach is directly applicable to the raw data (signal), and, thus, eliminates the need for a separate feature extraction algorithm resulting in more efficient systems in terms of both speed and hardware. Experimental results obtained using real motor data demonstrate the effectiveness of the proposed method for real-time motor condition monitoring.
引用
收藏
页码:7067 / 7075
页数:9
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