Using Deep Learning Based Approaches for Bearing Remaining Useful Life Predication

Jason Deutsch and David He
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_16_040.pdf459.88 KBSeptember 2, 2016 - 7:13am

Traditional data driven prognostics requires establishing explicit model equations and much prior knowledge about signal processing techniques and prognostic expertise, and therefore is limited in the age of big data. This paper presents a deep learning based approach for bearing remaining useful life (RUL) prediction with big data. This approach has the ability to automatically extract important features that can be used for RUL predictions. The presented approach is tested and validated using data collected from bearing run-to-failure tests and compared with existing PHM methods. The test results show the promising bearing RUL prediction performance of the deep learning based approach.

Publication Year: 
2016
Publication Volume: 
7
Publication Control Number: 
040
Page Count: 
7
Submission Keywords: 
bearing RUL prediction; deep learning; restricted boltzman machine
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Submitted by: 
  
 
 
 

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