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Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

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 blo...Show More

Abstract:

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.
Published in: IEEE Transactions on Industrial Electronics ( Volume: 63, Issue: 11, November 2016)
Page(s): 7067 - 7075
Date of Publication: 28 June 2016

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