The DOI matches its Crossref record.
Condition monitoring and prognosis of utility scale wind turbines
Abstract
The state of the art in condition monitoring in wind turbines, and related technologies currently applied in practice and under development for aerospace applications, are reviewed. Condition monitoring systems estimate the current condition of a machine from sensor measurements, whereas prognosis systems give a probabilistic forecast of the future condition of the machine under the projected usage conditions. Current condition monitoring practice in wind turbine rotors involves tracking rotor imbalance, aerodynamic asymmetry, surface roughness and overall performance and offline and online measurements of stress and strain. Related technologies for monitoring of load history and fatigue crack growth in aircraft structures are evaluated for their applicability to wind turbine blades. Similarly, condition monitoring practice in wind turbines is compared with monitoring and prognosis in helicopter gearboxes. The state of the art in condition monitoring of electronic controls, power electronics and towers is also evaluated and compared with the state of the art in aerospace. Based on these comparisons, technology needs and future challenges for the development of condition monitoring and prognosis for large wind machines, both onshore and offshore, are summarised.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Digital twin driven prognostics and health management for complex equipment
Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. C. (2018) · CIRP Annals · doi:10.1016/j.cirp.2018.04.055 · Cited by 1,000
Condition Monitoring of Wind Turbine Generators Using SCADA Data Analysis
Jin, X., Xu, Z., & Qiao, W. (2021) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2020.2989220 · Cited by 209
Metadata
- Authors
- Hyers, R. W., Mcgowan, J. G., Sullivan, K. L., Manwell, J. F., & Syrett, B. C.
- Venue
- Energy Materials
- Publisher
- Informa UK Limited
- Published
- 2006-09
- Volume
- 1
- Issue
- 3
- Pages
- 187-203
- ISSN
- 1748-9237 (ISSN-L)
- 1748-9245
- OpenAlex
- W2029783486
- Cited by
- 191
- References
- 0
- FWCI
- 3.00
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Condition monitoring and prognosis of utility scale wind turbines. https://app.cochat.ai/papers/10.1179/174892406x163397
- The paper
- Hyers, R. W., Mcgowan, J. G., Sullivan, K. L., Manwell, J. F., & Syrett, B. C. (2006). Condition monitoring and prognosis of utility scale wind turbines. Energy Materials. https://doi.org/10.1179/174892406x163397
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.