Trustworthy AI for Prognostics and Health Management of Electronic Equipment

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.

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