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

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 signific...

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Veröffentlicht in:IEEE transactions on industrial electronics (1982) Jg. 63; H. 11; S. 7067 - 7075
Hauptverfasser: Ince, Turker, Kiranyaz, Serkan, Eren, Levent, Askar, Murat, Gabbouj, Moncef
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.11.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Convolution
Convolutional neural networks (CNNs)
Fault detection
Feature extraction
Induction motors
Mathematical model
motor current signature analysis (MCSA)
Neural networks
Real-time systems
ISSN:0278-0046, 1557-9948
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2016.2582729