Fault Diagnosis and Prognosis for Electromechanical Actuators and Sensors

Dear Colleagues,

Elector-mechanical actuators (EMA) and sensors have been increasingly applied in aerospace due to their advantages of higher reliability, a lower weight and better maintainability. To guarantee the operational safety and reliability of EMAs and sensors, fault diagnosis and health management can be utilized to acquire reliable information on potential failures. Traditional machine learning (ML)-based fault diagnosis techniques are limited in their ability to process natural data in their raw form. In recent years, deep learning (DL) methods have been increasingly used in fault diagnosis and prediction. Deep learning is an algorithm based on data representation learning in machine learning. The most obvious difference between DL-based models and traditional DL-based models is that DL can learn the abstract representation features of the raw data automatically.

This Special Issue, “Fault Diagnosis and Prognosis for Electromechanical Actuators and Sensors”, seeks original research articles presenting novel approaches to DL-based fault diagnosis, DL-based fault prediction and health management using EMAs and sensors.

Dr. Yong Zhou
Prof. Dr. Chao Zhang
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. Sensors 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 2600 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.

• electromechanical actuators and sensors
• fault detection and fault diagnosis
• fault prediction and health management
• deep-learning based fault diagnosis method