Bearing Fault Diagnosis under Variable Speed Using Convolutional Neural Networks and the Stochastic Diagonal Levenberg-Marquardt Algorithm

Bearing Fault Diagnosis under Variable Speed Using Convolutional Neural Networks and the Stochastic Diagonal Levenberg-Marquardt Algorithm

This paper presents a novel method for diagnosing incipient bearing defects under variable operating speeds using convolutional neural networks (CNNs) trained via the stochastic diagonal Levenberg-Marquardt (S-DLM) algorithm. The CNNs utilize the spectral energy maps (SEMs) of the acoustic emission...

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Bibliographic Details
Main Authors: Viet Tra, Jaeyoung Kim, Sheraz Ali Khan, Jong-Myon Kim
Format: Article
Language:English
Published: MDPI AG 2017-12-01
Series:Sensors
Subjects:
acoustic emissions
bearing
fault diagnosis
convolutional neural networks
stochastic diagonal Levenberg-Marquardt algorithm
variable speed
Online Access:https://www.mdpi.com/1424-8220/17/12/2834

Internet

https://www.mdpi.com/1424-8220/17/12/2834

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