April 2023: This is an ongoing call for contributions to the DCE Special Collection on Reliability, Monitoring and Sensing Technology for Wind Energy.

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Amongst renewable sources in the global energy pool, wind energy holds the lead. However, the optimal operation and maintenance of wind energy infrastructure is non-trivial, because of its exposure to harsh environments, as well as repetitive, often extreme and highly variable loads. With a number of wind turbines currently reaching the end of their design span, the research and industrial communities need to turn to new methods and tools for reliable life-cycle assessment. Novel sensing and Non Destructive Evaluation solutions offer valuable tools for intelligent monitoring and assessment of wind energy infrastructure, at both the individual unit level, as well as the level of fleets - or populations.

Structural Health Monitoring (SHM) can be used for early stage verification and investigation of design uncertainties, deliver early warnings on degradation/damage and abnormal operation, as well as provide input for prognostic tasks, such as remaining useful lifetime assessment, preventive maintenance and optimisation of operational/control conditions. SHM applications include - amongst others - the measurement of the environmental inflow conditions, the turbine’s operational conditions, structural load effects (extreme and fatigue cycles), system dynamics (vibrations), mode shapes, natural frequencies and damping characteristics, stress “hot-spots”, etc. Applications, especially on the wind-farm level, of probabilistic machine learning and artificial intelligence algorithms, such as neural networks, Bayesian networks and ensemble classifiers are encouraged. Papers dealing with the following subjects are especially welcomed:

• Novel Sensing modalities coupled with advanced processing algorithms for condition assessment.
• Big Data applications for diagnostics, prognostics and decision support
• Multi-fidelity, multi-model aggregation, and ensemble approaches for inference and reliability analysis.
• Physics-constrained deep learning, model-driven and hybrid methods relying on fusion of data with models.
• Experimental investigations, Verification and Validation of assessment tools

We invite interested authors working on this theme to take part in a special collection of articles published by Data-Centric Engineering (cambridge.org/dce), a peer-reviewed, open access journal published by Cambridge University Press that is dedicated to the transformative impact of data science on all areas of engineering. 

Submitted articles will go through a single-blind review process. If accepted for publication, articles will be published in the journal and included on a page dedicated to this theme curated on the DCE website. 

Timeline

This is an ongoing special collection of papers. Authors should feel free to submit when ready. We aim for 90 days submission to decision time.

Submission process 

Full papers should be submitted through the DCE ScholarOne system. Please note the following key details, with more information available in the DCE Instructions for Authors.

Templates: DCE provides LaTeX, Overleaf and Word templates for authors. These do not have to be used for submission, but can be a useful prompt for key information. 

Article types: We anticipate that most submissions will be research articles, but the note that the journal also publishes translational papers, position papers and perspectives.

Abstract and Impact Statement: In addition to the Abstract (250 words) the article file should contain an impact statement (120 words describing the significance of the findings in language that can be understood by a wide audience).

Disclosure statements: At the end of the article text – ahead of the references – authors should provide the following statements:

• Competing interests: detail any financial, professional, contractual or personal situations that could be perceived to exert an undue influence on an authors’ presentation of their work, or state “None”. 
• Funding: provide details of any grant or other direct financial support the authors received to produce the work, or a statement that “This work received no specific grant from any funding agency, commercial or not-for-profit sectors.” 
• Data availability statement: describing where any data, code or replication materials can be accessed (including the DOI from the repository), or if these resources cannot be made publicly available the commercial or other reason for this. For more details and example data availability statements, see the journal’s policy on Transparency and Openness Promotion. 

When submitting your contribution please select the "Reliability, Monitoring and Sensing Technology" Special Collection in the drop down menu.

Please contact dce@cambridge.org with any queries about article preparation or submission.

Guest Editors 

• Eleni Chatzi (ETH Zurich) - DCE Editor-in-Chief
• Nikolaos Dervilis (University of Sheffield)
• Tanja Grießmann (Leibniz University of Hannover)
• Julio Javier Melero (University of Zaragoza) 
• Keith Worden (University of Sheffield)