AI-Based Machinery Health Monitoring

Dear Colleagues,

Advances in artificial intelligence are driving a paradigm shift in machinery health monitoring, enabling proactive maintenance and increased operational efficiency across diverse industries. By harnessing real-time sensor data, machine learning techniques, and cutting-edge technologies such as digital twins, AI-based monitoring systems can detect early signs of degradation, accurately diagnose faults, and predict failures before they lead to costly downtime. This holistic approach—combining intelligent data analytics at the cloud and edge, sophisticated anomaly detection methods, and new forms of human–machine collaboration—represents a transformative leap from traditional time-based maintenance toward truly predictive and prescriptive strategies.

This Special Issue seeks original research articles, case studies, and review papers covering the latest advancements and future trends in AI-based machinery health monitoring. Potential topics include, but are not limited to:

• Machine learning, deep learning, and hybrid techniques for fault diagnosis and prognostics.
• Large Language Models (LLMs), agenting workflows and frameworks for anomaly detection, predictive maintenance, and intelligent fault diagnosis.
• Advanced sensing technologies, Internet of Things (IoT) integration, and edge/cloud architectures.
• Digital twin development for real-time monitoring and “what-if” simulations.
• Remaining useful life (RUL) estimation and performance optimization.
• Applications of augmented/virtual reality in maintenance tasks.
• Industry 4.0/5.0 concepts and sustainable, energy-efficient maintenance solutions.

Manuscripts must be original, high-quality contributions that have not been previously published or are not under consideration elsewhere. Extended versions of conference papers are welcome, provided they demonstrate significant new content and clear improvements over the original work.

We look forward to receiving your contributions.

Dr. Alexandru Stanciu
Dr. Rui Araújo
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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences 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.