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Electronics
Special Issues
Trustworthy AI for Prognostics and Health Management of Electronic Equipment
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Trustworthy AI for Prognostics and Health Management of Electronic Equipment

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 590

Special Issue Editors

Dr. Jipu Li

E-Mail Website
Guest Editor
Department of Industrial and System Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: industrial intelligence; industrial big data; intelligent maintenance and health management
Dr. Quanxue Guan

E-Mail Website
Guest Editor
School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen 518107, China
Interests: power electronic and machine control; artificial intelligence applications; power and transportation nexus
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoli Zhao

E-Mail Website
Guest Editor
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: intelligent operation and maintenance and health management for high-end electromechanical and hydraulic equipment; artificial intelligence and signal processing; digital twins and physical information systems
Special Issues, Collections and Topics in MDPI journals
Dr. Liqiao Xia

E-Mail Website
Guest Editor
Department of Mechanical & Aerospace Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
Interests: reliability analysis; predictive maintenance; knowledge graph; human robot collaboration

Special Issue Information

Dear Colleagues,

With the widespread application of electronic equipment in fields such as industrial automation, aerospace, healthcare, and consumer electronics, their reliability and lifespan have become critical issues. Prognostics and health management (PHM) offers effective solutions for predicting equipment failures and extending their lifespan. In recent years, with the rapid advancement of artificial intelligence (AI) technology, AI-driven PHM systems have shown tremendous potential in enhancing the predictive capabilities and management efficiency of equipment. As the reliance on electronic systems increases across various industries, the demand for reliable and trustworthy AI solutions to ensure their optimal performance and safety has become paramount. However, the issue of 'trustworthiness' in AI models, particularly in terms of transparency, fairness, safety, and reliability, remains a significant challenge.

This Special Issue seeks original contributions that address the challenges of developing trustworthy AI solutions for PHM in electronic equipment. We invite research that focuses on explainability, transparency, reliability, and robustness in AI models, as well as their applications in PHM. Submissions may explore the use of AI in fault detection, diagnosis, and prognostics, along with innovative approaches that ensure the safety and reliability of AI-driven PHM systems.

Topics of interest include, but are not limited to, the following:

  • Trustworthy AI models for predictive maintenance in electronic systems.
  • Explainable AI techniques in PHM for electronic equipment.
  • Robust and reliable AI models for PHM.
  • Uncertainty quantification in AI-driven PHM systems.
  • AI-based predictive maintenance in industrial electronics.
  • Digital twin-assisted reliable AI models for electronic equipment.
  • Data-driven approaches for health assessment and remaining useful life estimation of electronic components.
  • Security and privacy preservation in AI-based electronic equipment health management.
  • Case studies demonstrating the implementation of trustworthy AI in electronic PHM solutions.

Dr. Jipu Li
Dr. Quanxue Guan
Dr. Xiaoli Zhao
Dr. Liqiao Xia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • trustworthy AI
  • explainable AI
  • PHM
  • electronic equipment
  • digital twin

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

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Research

18 pages, 1143 KiB  
Article
A Real-Time Downhole Fluid Identification System Empowered by Efficient Quadratic Neural Network
by Zhongshuai Chen, Hongjian Ni, Xueliang Pei and Shiping Zhang
Electronics 2024, 13(24), 5021; https://doi.org/10.3390/electronics13245021 - 20 Dec 2024
Viewed by 357
Abstract
In the petroleum industry, accurately identifying downhole fluid is crucial for understanding fluid composition and estimating crude oil contamination and other properties. Near-infrared (NIR) spectrum analysis technology has achieved successful fluid identification applications due to its non-destructive nature and high efficiency. However, for [...] Read more.
In the petroleum industry, accurately identifying downhole fluid is crucial for understanding fluid composition and estimating crude oil contamination and other properties. Near-infrared (NIR) spectrum analysis technology has achieved successful fluid identification applications due to its non-destructive nature and high efficiency. However, for real-time downhole fluid analysis, the NIR spectrometer faces challenges such as miniaturization and cost effectiveness. To address these issues, we construct a real-time downhole fluid identification system in this work. First, we propose a lightweight and deployable fluid identification model by integrating the successive projections algorithm (SPA) and a quadratic neural network (QNN). The SPA allows for spectral feature selection, and the QNN acts as an efficient identification model. Consequently, we use only four specific wavelengths with a one-hidden-layer QNN to achieve high identification accuracy. Second, we devise a hardware deployment scheme for real-time identification. We use four laser diodes to replace conventional light sources, further saving space. The QNN is then deployed to the STM32 MCU to implement real-time identification. Computational and online experiments demonstrate that our system functions well in real-time fluid identification and can further estimate the oil contamination rate with acceptable error. Full article
(This article belongs to the Special Issue Trustworthy AI for Prognostics and Health Management of Electronic Equipment)
►▼ Show Figures

Figure 1

Figure 1
<p>The overall fluid composition identification framework. (<b>a</b>) The optical spectra of the fluid are acquired via a spectrometer. (<b>b</b>) The collected data are fed to the end-to-end composition identification network for training. (<b>c</b>) The fine-tuned network is deployed to the edge device for real-time identification purposes.</p> Full article ">Figure 2
<p>The structural diagram of the optical system. Four laser diodes (LDs) with specific wavelengths are adopted as the light source. A flow cell with an optical path length of 5 mm for dynamic drilling fluid. An InGaAs photodiode is employed as the light receiver, sensitive wavelengths of which range from 400 nm to 1700 nm.</p> Full article ">Figure 3
<p>The main program flow chart of a real-time composition identification system.</p> Full article ">Figure 4
<p>Spectral acquisition platform for computational experiments.</p> Full article ">Figure 5
<p>The spectra of all measured fluid. (<b>a</b>) The spectra of different types of oil and OBM, where the absorbance peaks of oils occur at around 1200 nm and 1400 nm. (<b>b</b>) The spectra of water and WBM, where the absorbance peaks of water emerge over 1440 nm.</p> Full article ">Figure 6
<p>The relation between the number of wavelengths determined by SPA and identification accuracy.</p> Full article ">Figure 7
<p>The spectra of different types of fluids.</p> Full article ">Figure 8
<p>Comparison of the extracted features of last neural network layers using TSNE.</p> Full article ">Figure 9
<p>The edge platform for static fluid identification experiments.</p> Full article ">Figure 10
<p>The process of measurement-while-drilling.</p> Full article ">Figure 11
<p>The edge platform for online measurement experiments.</p> Full article ">Figure 12
<p>The slug flow identification results of our device.</p> Full article ">
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