Intelligent fault detection using raw vibration signals via dilated convolutional neural networks

Fault detection and diagnosis is critical to improve the reliability and availability in induction motors (IMs). Machine learning and deep learning techniques have been widely used in induction motor fault detection and diagnosis. In this paper, we propose a new deep learning model based on a dilate...

Full description

Saved in:
Bibliographic Details
Published in:The Journal of supercomputing 2020-10, Vol.76 (10), p.8086-8100
Main Authors: Khan, Mohammad Azam, Kim, Yong-Hwa, Choo, Jaegul
Format: Article
Language:English
Subjects:
Artificial neural networks
Compilers
Computer Science
Deep learning
Fault detection
Fault diagnosis
Feature extraction
Induction motors
Interpreters
Machine learning
Network reliability
Neural networks
Processor Architectures
Programming Languages
Signal classification
Signal processing
Vibration
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Fault detection and diagnosis is critical to improve the reliability and availability in induction motors (IMs). Machine learning and deep learning techniques have been widely used in induction motor fault detection and diagnosis. In this paper, we propose a new deep learning model based on a dilated convolutional neural network (D-CNN) for detecting bearing faults in IMs. The proposed model works directly on raw vibration signals without any hand-crafted feature extraction process. Our model can incorporate global context without losing important local information by stacking dilated convolutions with an increasing width. Numerical results show that the proposed D-CNN is not only capable of classifying normal signals perfectly but also can achieve higher accuracy than conventional techniques under noisy environments.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-018-2711-0