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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Vydané v:IEEE transactions on industrial electronics (1982) Ročník 63; číslo 11; s. 7067 - 7075
Hlavní autori: Ince, Turker, Kiranyaz, Serkan, Eren, Levent, Askar, Murat, Gabbouj, Moncef
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.11.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2016.2582729