Forthcoming Special Issues
Emerging Physics-Enhanced Machine Learning Methods Enabling SHM of Civil Engineering Structures
Structural Health Monitoring (SHM) systems play an essential role in the field of civil engineering, especially for assessing safety conditions involving critical structures, such as bridges, high-rise buildings, tunnels, wind turbine towers and historic buildings, just to mention a few.
Successful SHM strategies must integrate real-time and reliable data acquisition, robust feature extraction from the acquired data, coherent statistical modeling of the features, and classification of the features to make informed decisions. However, the SHM challenge in Civil Engineering is unique, characterized by a lack of labeled data, training examples, and cost-prohibitive testing. A solution is to leverage machine learning models and integrate physical knowledge to accelerate convergence and produce actionable data, also known as physics-enhanced machine learning (PEML).
This Special Issue aims at stimulating discussions through state-of-the-art contributions on the latest research and development, up-to-date issues, and challenges, and to inform on emerging PEML methods engineered for the SHM of Civil Engineering Structures.
Guest editors:
Filippo Ubertini
University of Perugia, Italy
Alexandre Cury
Federal University of Juiz de Fora, Brazil
Diogo Ribeiro
Polytechnic Institute of Porto, Portugal
Simon Laflamme
Iowa State University, United States of America
Manuscript submission information:
Manuscript submission deadline: 31 December 2022.
You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact Prof. Filippo Ubertini via [email protected].
Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: Physics-Enhanced ML for SHM” when submitting your manuscript online at the journal’s submission platform Editorial Manager®. Both the Guide for Authors and the submission portal could also be found on the Journal Homepage here: https://www.sciencedirect.com/journal/mechanical-systems-and-signal-processing.
Keywords:
Structural Health Monitoring; Physics-Enhanced Machine Learning; Damage Identification; Digital Twins
Learn more about the benefits of publishing in a special issue: https://www.elsevier.com/authors/submit-your-paper/special-issues
Interested in becoming a guest editor? Discover the benefits of guest editing a special issue and the valuable contribution that you can make to your field: https://www.elsevier.com/editors/role-of-an-editor/guest-editors
Advanced Nonstationary Signal Processing Algorithms and Techniques for Machinery Fault Diagnosis and Prognosis
Industrial machinery undergoes inevitable health degradation, which affects its performance and structural integrity. Timely diagnosis and prognosis of the degradation symptoms are essential to support predictive maintenance decision-making and to guarantee industrial safety and productivity. Variable operating conditions are often encountered in industrial machinery. For instance, wind turbine gearboxes operate under random speed and load due to the randomness of wind speed and direction, while train traction gearboxes operate under variable speed and load when the train passes through high-curvature areas. The variable operating conditions may accelerate the degradation process of machinery and manifest in the condition monitoring data, which impede the diagnosis and prognosis of the machinery. Developing methods to effectively and efficiently process the nonstationary condition monitoring data to achieve accurate and reliable fault diagnosis and prognosis under variable operating conditions has drawn much interest in the past decade.
The overarching intention of this special issue is to present works dealing mainly (but not exclusively) with state-of-the-art solutions of signal processing, dynamics modelling, and artificial intelligence for machinery diagnostics and prognostics.
Guest editors:
Yuejian Chen
Tongji University, China
Ke Feng
University of British Columbia, Canada
Stephan Schmidt
University of Pretoria, South Africa
Stephan Heyns
University of Pretoria, South Africa
Gang Niu
Tongji University, China
Special issue information:
Prospective authors are invited to submit high-quality original contributions or reviews to this Special Issue. Potential topics include, but are not limited to:
- Variable operating condition information extraction from nonstationary signals that can aid with diagnosis and prognosis.
- Time-variant system identification based on nonstationary responses to represent the underlying data generating process
- Time-frequency analysis of the nonstationary signals for the extraction of fault-related signatures
- Adaptive signal decomposition for the extraction of fault-related signatures
- Machine learning algorithms for nonstationary operating conditions
- Prediction of machinery remaining useful life considering variable operating conditions
Manuscript submission information:
Manuscript submission deadline: 25 February 2023.
You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact Dr. Yuejian Chen via [email protected].
Please refer to the Guide for Authors to prepare your manuscript, and select the article type of "VSI: NonStatSigProcAlgo&Tech" when submitting your manuscript online at the journal’s submission platform Editorial Manager®. Both the Guide for Authors and the submission portal could also be found on the Journal Homepage: https://www.sciencedirect.com/journal/mechanical-systems-and-signal-processing.
Keywords:
Machinery; Nonstationary Signal Processing; Fault Diagnosis; Fault Prognosis
Why publish in this Special Issue?
- Special Issue articles are published together on ScienceDirect, making it incredibly easy for other researchers to discover your work.
- Special content articles are downloaded on ScienceDirect twice as often within the first 24 months than articles published in regular issues.
- Special content articles attract 20% more citations in the first 24 months than articles published in regular issues.
- All articles in this special issue will be reviewed by no fewer than two independent experts to ensure the quality, originality and novelty of the work published.
Learn more about the benefits of publishing in a special issue: https://www.elsevier.com/authors/submit-your-paper/special-issues
Interested in becoming a guest editor? Discover the benefits of guest editing a special issue and the valuable contribution that you can make to your field: https://www.elsevier.com/editors/role-of-an-editor/guest-editors