Remaining Useful Life Estimation for Anti-friction Bearing Prognosis Based on Envelope Spectrum and Variational Autoencoder

Haobin Wen, Long Zhang, Jyoti Sinha, Khalid Almutairi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Anti-friction bearings (AFB) are crucial structural components conveying rotating motions in a variety of mechanical systems. To avoid unscheduled breakdowns and fatal failures, remaining useful life (RUL) prediction is of great practical significance in industrial practice for prognostics health management, e.g., optimizing maintenance plan for component replacements. Recently, the artificial intelligence (AI) advancements have provided effective data-driven models for bearing prognostics using machine learning. In this paper, using the variational auto-encoder (VAE) networks as the regression backbone, the bearing RUL is estimated using envelope spectra via measured vibrational data. First, the envelope spectra are utilized for bearing fault detection and the network input features. After the fault is detected, the VAE is used for learning the probabilistic mapping from the spectral input to the estimated RUL value, given its good probabilistic and generative properties over the classical auto-encoder (AE) in content generation and variational inference. The application of the method to the run-to-failure measured vibration data from the experimental rig available online have shown its efficacy in bearing RUL estimation.

Original languageEnglish
Title of host publicationInternational Congress and Workshop on Industrial AI and eMaintenance 2023
EditorsUday Kumar, Ramin Karim , Diego Galar, Ravdeep kour
PublisherSpringer Nature
Number of pages13
ISBN (Electronic)2195-4364
ISBN (Print)9783031396182
Publication statusPublished - 2 Jan 2024

Publication series

NameLecture Notes in Mechanical Engineering


  • Bearing
  • Machine learning
  • Prognostics health management
  • Remaining useful life
  • Variational auto-encoder


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