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Modeling of Machine Life Using Accelerated Prognostics and Health Management (APHM) and Enhanced Deep Learning Methodology

Jin, Wenjing
http://orcid.org/0000-0003-3216-1856
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479821186023747

Abstract Details

2016, PhD, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
Machine health monitoring has advanced significantly for improving machine uptime and efficiency by providing proper fault detection and remaining useful life (RUL) prediction information to machine users. Despite these advancements, conventional condition monitoring (CM) techniques face several challenges in machine prognostics, including the ineffective RUL prediction modeling for machine under dynamic working regimes, and the lack of complete lifecycle data for modeling and validation, among others. To address these issues, this research introduces Accelerated Degradation Tests (ADT) with a deep learning technique, which is a novel method to improve machine life prediction accuracy under different working regimes for Prognostics and Health Management applications. This dissertation work highlights the mathematical framework of deep learning based machine life modeling under an ADT environment, including Constant Stress Accelerated Degradation Testing (CSADT) and Step-Stress ADT (SSADT) conditions. Since most CM features show no trend or indication of failure until a machine is approaching the end of its life, current RUL prediction techniques are not applicable in that they are only effective when incipient degradation is detected. This dissertation work applies feature enhancement to condition-based features using the enhanced Restricted Boltzmann Machine (RBM) method with a prognosability regularization term; afterwards, a similarity-based method is applied to predict machine life with the enhanced RBM features. In addition, this research has added varying stress conditions during experiments to replicate dynamic operation regimes. The stress variable, a type of regime variables, is input into Mixed-Variate RBM (MV-RBM) model. Therefore, a Regime Matrix based RBM (RM-RBM) is proposed to improve the feature prognosability and reduce the impact that the working stresses have on the features. Then the RBM features can be fused into a single health value which reflects the machine degradation. Finally, the developed machine “life-stress-degradation” model can effectively estimate the machine life under any given stress. The feasibility study is demonstrated through three groups of rotary machinery components run-to-failure tests datasets. The first two case studies focus on CSADT from two bearing run-to-failure test-beds. The first in-house bearing test demonstrates the effectiveness of applying an enhanced RBM with a prognosability regularization term to improve predictability of both the features and health value. The second bearing test dataset utilizes a similarity-based method to benchmark the RUL prediction results of the RBM features with other feature extraction methods. The third case study focuses on the issue of dynamic operating regimes; it is validated through a step-stress accelerated degradation test on a ball screw system. By integrating both the RM-RBM model and SSADT model in the PHM analysis framework, an innovative condition-based life-stress model for the linear motion system will be demonstrated.
Jay Lee, Ph.D. (Committee Chair)
Linxia Liao, Ph.D. (Committee Member)
Teik Lim, Ph.D. (Committee Member)
David Thompson, Ph.D. (Committee Member)
155 p.
Mechanical Engineering; Mechanics
Prognostics and Health Management; Accelerated Life Testing; Deep Learning; Restricted Boltzmann Machine; RUL Prediction; Condition Monitoring

Recommended Citations

Citations

  • Jin, W. (2016). Modeling of Machine Life Using Accelerated Prognostics and Health Management (APHM) and Enhanced Deep Learning Methodology [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479821186023747

    APA Style (7th edition)

  • Jin, Wenjing. Modeling of Machine Life Using Accelerated Prognostics and Health Management (APHM) and Enhanced Deep Learning Methodology. 2016. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479821186023747.

    MLA Style (8th edition)

  • Jin, Wenjing. "Modeling of Machine Life Using Accelerated Prognostics and Health Management (APHM) and Enhanced Deep Learning Methodology." Doctoral dissertation, University of Cincinnati, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479821186023747

    Chicago Manual of Style (17th edition)

ucin1479821186023747
1,145
© 2016, some rights reserved.Creative Commons License Modeling of Machine Life Using Accelerated Prognostics and Health Management (APHM) and Enhanced Deep Learning Methodology by Wenjing Jin is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.