Recent Advances in Fault Diagnostics, Prognostics, and Intelligent Condition-Based Maintenance

Dear Colleagues,

We are pleased to invite you to submit papers to the Topical Collection of Sensors on “Recent Advances in Fault Diagnostics, Prognostics, and Intelligent Condition-Based Maintenance”. The reliability and availability of assets can largely be improved through the application of real-time condition monitoring and condition-based maintenance. Recent advancements in smart sensor technology along with digitalization and the Industrial Internet of Things (IIoT) offer significant advantages for these applications as never before. Empowered by the increasing computational power broadly available in modern computers, recent theoretical developments in artificial intelligence (AI) and advanced machine learning capabilities have broken new ground. Indeed, they enable the conversion of massive and multidimensional sensor data into useful information and provide new perspectives for meaningful fault diagnostics and prognostics of remaining useful life (RUL). These outstanding advancements put us one step closer to implementing digital twins that replicate physical systems using real-time digital models, which would enable operators to continuously analyze performance, optimize control and operation, and refine an asset’s intelligent condition-based maintenance across its lifecycle.

Given the above premises, this Topical Collection aims at highlighting the recent trends, research and developments, applications, solutions, and challenges of fault diagnostics and prognostics in intelligent condition-based maintenance. All submissions will be peer-reviewed and selected based on both their novelty and relevance. Both theoretical and application-oriented contributions are welcome, together with review articles on specific subjects within the scope of this Collection. Potential topics of interest include but are not necessarily limited to the following:

• Smart sensor systems applied to fault detection and diagnosis;
• Application of AI and big data analysis in diagnostics and prognostics;
• Data-driven, physics-based model, and hybrid approaches for diagnostics and prognostics;
• Digital twin-assisted condition monitoring;
• Wireless sensor networks and IIOT for remote condition monitoring applications;
• Advanced sensing and structural health monitoring;
• Decision making in intelligent condition-based maintenance.

Dr. Hamed Badihi
Dr. Tao Chen
Dr. Ningyun Lu
Collection 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.