Supervised Health Stage Prediction Using Convolutional Neural Networks for Bearing Wear

Supervised Health Stage Prediction Using Convolutional Neural Networks for Bearing Wear

Early detection of faults in rotating machinery systems is crucial in preventing system failure, increasing safety, and reducing maintenance costs. Current methods of fault detection suffer from the lack of efficient feature extraction method, the need for designating a threshold producing minimal f...

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Bibliographic Details
Main Authors: Sungho Suh, Joel Jang, Seungjae Won, Mayank Shekhar Jha, Yong Oh Lee
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Sensors
Subjects:
fault detection
convolutional neural networks
feature extraction
machinery prognostics
health stage division
Online Access:https://www.mdpi.com/1424-8220/20/20/5846

Internet

https://www.mdpi.com/1424-8220/20/20/5846

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