Campus Units

Civil, Construction and Environmental Engineering, Electrical and Computer Engineering, Center for Nondestructive Evaluation (CNDE)

Document Type

Conference Proceeding

Conference

2021 IEEE International Conference on Prognostics and Health Management (ICPHM)

Publication Version

Accepted Manuscript

Link to Published Version

https://doi.org/10.1109/ICPHM51084.2021.9486650

Publication Date

2021

Journal or Book Title

2021 IEEE International Conference on Prognostics and Health Management (ICPHM)

Research Focus Area

Structural Engineering

DOI

10.1109/ICPHM51084.2021.9486650

Conference Title

2021 IEEE International Conference on Prognostics and Health Management (ICPHM)

Conference Date

June 7-9, 2021

City

Detroit (Romulus), MI

Abstract

Failure prognostics is the process of predicting the remaining useful life (RUL) of machine components, which is vital for the predictive maintenance of industrial machinery. This paper presents a new deep learning approach for failure prognostics of rolling element bearings based on a Long Short-Term Memory (LSTM) predictor trained simultaneously within a Generative Adversarial Network (GAN) architecture. The LSTM predictor takes the current and past observations of a well-defined health index as an input, uses those to forecast the future degradation trajectory, and then derives the RUL. Our proposed approach has three unique features: (1) Defining the bearing failure threshold by adopting an International Organization of Standardization (ISO) standard, making the approach industry-relevant; (2) Employing a GAN-based data augmentation technique to improve the accuracy and robustness of RUL prediction in cases where the deep learning model has access to only a small amount of training data; (3) Integrating the training process of the LSTM predictor within the GAN architecture. A joint training approach is utilized to ensure that the LSTM predictor model learns both the original and artificially generated data to capture the degradation trajectories. We utilize a publicly available accelerated run-to-failure dataset of rolling element bearings to assess the performance of the proposed approach. Results of a five-fold cross-validation study show that the integration of the LSTM predictor with GAN helps to decrease the average RUL prediction error by 29% over a simple LSTM model without GAN implementation.

Comments

This is a manuscript of a proceeding published as Lu, Hao, Vahid Barzegar, Venkat P. Nemani, Chao Hu, Simon Laflamme, and Andrew T. Zimmerman. "GAN-LSTM Predictor for Failure Prognostics of Rolling Element Bearings." In 2021 IEEE International Conference on Prognostics and Health Management (ICPHM), IEEE, 2021. DOI: 10.1109/ICPHM51084.2021.9486650. Posted with permission.

Rights

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright Owner

IEEE

Language

en

File Format

application/pdf

Published Version

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