Fault Diagnosis and Prognosis in Actuators
A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Control Systems".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 97
Special Issue Editors
Interests: system fault diagnosis and prognosis; testability design and safety analysis; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: system fault diagnosis and prognosis; flight control system; unmanned aerial vehicle
Special Issues, Collections and Topics in MDPI journals
Interests: system fault diagnosis and prognosis; testability design and safety analysis; flight control and actuator design
Special Issue Information
Dear Colleagues,
Various novel types of actuators are widely being applied in fields such as aerospace, marine, automotive, energy and power. As the primary components of systems, their long-term reliable operation is crucial; however, actuator faults are common in practical systems, which significantly affect quality and efficiency, potentially leading to serious safety incidents. Therefore, the diagnosis and prognosis of actuator faults is becoming increasingly important yet challenging.
This Special Issue aims to consolidate the latest research and developments in fault diagnosis and prognosis in actuators. We will explore innovative methodologies and technologies to be used for real-time fault detection, rapid fault isolation, accurate fault identification, efficient health management, and predictive maintenance and the application of artificial intelligence in fault diagnosis and prognosis.
We welcome contributions that explore theoretical models, computational studies, experimental validations, and practical implementations across various complex engineering domains. We encourage the submission of papers on a broad range of issues, including, but not limited to, the following:
- The fault detection of various actuators;
- The fault isolation of different actuators;
- The fault identification of various actuators;
- Fault diagnosis and prognosis based on signal processing;
- Fault diagnosis and prognosis based on theoretical models;
- Data-driven fault diagnosis and prognosis;
- Machine learning and artificial intelligence in fault diagnosis and prognosis;
- Typical application cases.
Prof. Dr. Chao Zhang
Prof. Dr. Zhenbao Liu
Prof. Dr. Jie Chen
Dr. Qingbin Gao
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. Actuators is an international peer-reviewed open access monthly 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
- actuators
- fault diagnosis
- fault prognosis
- fault detection
- fault isolation
- fault identification
- signal processing
- data-driven
- machine learning
- artificial intelligence
- health management
- predictive maintenance
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