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Advances in Mechatronic Systems Health Monitoring and Fault Diagnosis
This special issue belongs to the section “Mechanical Engineering“.
Special Issue Information
Dear Colleagues,
Mechatronic systems are essential in modern engineering applications as they integrate mechanical components, electrical drives, sensors, power electronics, and control software into intelligent platforms. This high level of integration enables superior performance and advanced functionality. However, it also leads to tightly coupled multiphysics interactions and the generation of large volumes of data, which complicate system monitoring and analysis. In real-world operation, faults may originate from a wide range of sources, including mechanical wear, misalignment, lubrication degradation, electrical failures, sensor drift, and control-related issues. Recent progress in smart sensing, the Industrial Internet of Things (IIoT), edge and embedded computing, digital twins, and artificial intelligence has significantly improved the ability to monitor these systems continuously and detect early signs of degradation. However, achieving reliable diagnosis and prognosis methods that can generalize across different operating regimes, remain interpretable, and be deployed in real time is still an open challenge.
We are pleased to invite you to contribute to this Special Issue, which aims to bring together new methodologies and validated applications that improve fault detection, diagnosis, and prognostics (including remaining useful life estimation) for modern mechatronic assets.
Topics include, but are not limited to, the following:
- Advanced signal processing and time–frequency analysis for multi-domain measurements.
- AI/ML and deep learning for fault detection, diagnosis, and prognostics (including physics-informed and explainable AI).
- Model-based and hybrid approaches (digital twins, observer-based diagnosis, parameter estimation).
- Multisensor/multi-physics data fusion (vibration, current, AE, thermal, vision, etc.)
- Edge, embedded, and FPGA/real-time implementations for online monitoring.
- Industrial case studies in robotics, manufacturing, energy, transportation, and autonomous systems.
Dr. Israel Zamudio-Ramírez
Dr. José-Roberto Razo-Hernández
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.
Keywords
- mechatronic systems
- condition monitoring
- fault diagnosis
- prognostics and health management (PHM)
- remaining useful life (RUL)
- multisensor data fusion
- time–frequency analysis
- deep learning
- digital twins
- physics-informed AI
- edge computing
- FPGA implementation
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