Selected Papers from the Asia Pacific Conference of the Prognostics and Health Management Society 2021

A special issue of Electronics (ISSN 2079-9292).

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 3392

Special Issue Editors

School of Mechanical Engineering, Hanyang University, Seoul 04763, Korea
Interests: characterizing degradation and evolution of energy materials; degradation prognostics of energy materials
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Guest Editor
School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Korea
Interests: smart manufacturing systems; advanced manufacturing processes; data-driven design

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Guest Editor
Department of Systems Management Engineering, Sungkyunkwan University, Suwon 16419, Korea
Interests: prognostics and health management for electronics; additive manufacturing for reliability; smart IoT sensors

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Guest Editor
The Cho Chun Shik Graduate School of Green Transportation, Korea Advanced Institute of Science and Technology, 34141 Daejeon, Korea
Interests: generative design; data-driven design; artificial intelligence; design optimization

Special Issue Information

Dear Colleagues,

It is our great honor to invite you to attend the third Asia Pacific Conference of the Prognostics and Health Management Society (PHM Asia Pacific 2021), hosted by the Korean Society for Prognostics and Health Management (KSPHM) and PHM Society. The PHM Asia Pacific 2021 will be held from September 8 to 11 at Jeju Island, Republic of Korea. The theme of the PHM Asia Pacific 2021 is “Pioneering a Digital Journey with PHM”. As COVID-19 continues to sweep across the globe, “untact” activities are becoming the new normal. As a consequence, digital technologies including AI, big data, IoT, and so on are playing a key role in our daily lives. The PHM Asia Pacific 2021, with researchers’ latest works and inspiring ideas, will serve as a platform to experience PHM technology as a way of pioneering a digital journey. Prominent scholars, key engineers, and industrial experts from diverse industrial sectors will be brought together to have an in-depth discussion about PHM technologies in a digital transformation era. We would like to welcome all attendees to the PHM Asia Pacific 2021 and look forward to your participation and support. This Special Issue will be composed of selected papers from the PHM Asia Pacific 2021, extended and reviewed to meet the high standard of journal publications.

Conference topics include but are not limited to:

  • PHM Theory and Methods:
    • Big data analytics and uncertainty analysis
    • Diagnostics and prognostics methods
    • Model based, data-driven, and hybrid PHM
    • Machine learning methods for PHM
    • Deep learning methods for PHM
    • Condition-based and predictive maintenance
    • PHM cost benefit analysis
    • Physics-of-failures for PHM
  • PHM Design
    • Design for IoT devices
    • Design for data acquisition and management
    • Design for PHM verification and validation
    • Design for PHM methodology
    • Design for PHM digital twin
  • PHM Applications
    • PHM for mobility
    • PHM for manufacturing
    • PHM for defense and Space
    • PHM for energy and utility
    • PHM for software
    • PHM for semiconductor and electronics
  • PHM Standards, Education, and Others
    • Codes and standards
    • PHM curriculum
    • Risk assessment and management
    • Accelerated life and degradation analysis
    • Reliability analysis and design

Prof. Dr. Ki-Yong Oh
Prof. Dr. Sang Won Lee
Prof. Dr. Daeil Kwon
Prof. Dr. Namwoo Kang
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • PHM Theory and Methods
  • PHM Design
  • PHM Applications
  • PHM Standards, Education, and Others

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Published Papers (1 paper)

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Research

20 pages, 7283 KiB  
Article
A Wavelet-Based Diagnostic Framework for CRD Engine Injection Systems under Emulsified Fuel Conditions
by Ugochukwu Ejike Akpudo and Jang-Wook Hur
Electronics 2021, 10(23), 2922; https://doi.org/10.3390/electronics10232922 - 25 Nov 2021
Cited by 1 | Viewed by 2169
Abstract
The impact of the constituent oxides of nitrogen, carbon, sulphur, and other particulate matter which make up the gas emissions from diesel engines has motivated several control techniques for these pollutants. Water-in-diesel emulsions provide a reliable solution, but the wear effects on the [...] Read more.
The impact of the constituent oxides of nitrogen, carbon, sulphur, and other particulate matter which make up the gas emissions from diesel engines has motivated several control techniques for these pollutants. Water-in-diesel emulsions provide a reliable solution, but the wear effects on the fuel injection system (FIS) still pose remarkable concerns. Because pressure signals from the common rail (CR) reflect the dynamics associated with varying emulsion compositions and at varying engine RPMs, an investigative (and diagnostic) study was conducted on a KIA Sorento 2004 four-cylinder line engine at various water-in-diesel emulsion compositions and engine speeds. Alongside visual/microscopic inspections and spectral analyses, the diagnostic framework proposed herein functions on the use of standardized first-order differentials of the CR pressure signals to generate reliable continuous wavelet coefficients (CWCs) which capture discriminative spectral and transient information for accurate diagnosis. The results show that by extracting the CWCs from the first-order CR pressure differentials up to the 512th scale on a Mexican hat wavelet, adequate fault parameters can be extracted for use by a deep neural network (DNN) whose hyperparameters were globally optimized following a grid search. With a test accuracy of 92.3% against other widely-used ML-based diagnostic tools, the proposed DNN-based diagnostics tool was empirically assessed using several performance evaluation metrics. Full article
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