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Taxonomy of Gas Turbine Blade Defects
Open accessAbstract
Context—The maintenance of aero engines is intricate, time-consuming, costly and has significant functional and safety implications. Engine blades and vanes are the most rejected parts during engine maintenance. Consequently, there is an ongoing need for more effective and efficient inspection processes. Purpose—This paper defines engine blade defects, assigns root-causes, shows causal links and cascade effects and provides a taxonomy system. Approach—Defect types were identified from the literature and maintenance manuals, categorisations were devised and an ontology was created. Results—Defect was categorised into Surface Damage, Wear, Material Separation and Material Deformation. A second categorisation identified potential causes of Impact, Environmental causes, Operational causes, Poor maintenance, Poor manufacturing and Fatigue. These two categorisations were integrated with an ontology. Originality—The work provides a single comprehensive illustrated list of engine blade defects, and a standardised defect terminology, which currently does not exist in the aviation industry. It proposes a taxonomy for both engine blade defects and root-causes, and shows that these may be related using an ontology.
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“The most common edge defects are dents, nicks and tears.”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [15]
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Citations
Cites
Common failures in gas turbine blades
Carter, T. J. (2004) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2004.07.004 · Cited by 458
High cycle fatigue in aircraft gas turbines—an industry perspective
Cowles, B. A. (1996) · International Journal of Fracture · doi:10.1007/bf00012667 · Cited by 422
Handbook of Semantic Web Technologies
Domingue, J., Hendler, J., & Fensel, D. (2011) · Springer eBooks · doi:10.1007/978-3-540-92913-0 · Cited by 238
Hou, J., Wicks, B. J., & Antoniou, R. A. (2002) · Engineering Failure Analysis · doi:10.1016/s1350-6307(01)00005-x · Cited by 211
Causal Mapping for Research in Information Technology
(2005) · IGI Global eBooks · doi:10.4018/978-1-59140-396-8 · Cited by 113
Failure of first stage gas turbine blades
Khajavi, M., & Shariat, M. H. (2004) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2003.08.010 · Cited by 104
Failure analysis of an un-cooled turbine blade in an aero gas turbine engine
Mishra, R. K., Thomas, J., Srinivasan, K., Nandi, V., & Raghavendra Bhatt, R. (2017) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2017.05.042 · Cited by 85
The development of an ontology-based expert system for corporate financial rating
Shue, L., Chen, C., & Shiue, W. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.12.044 · Cited by 84
Failure analysis of gas turbine rotor blades
Kumari, S., Satyanarayana, D. V. V., & Srinivas, M. (2014) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2014.06.003 · Cited by 82
Erosion-fatigue of steam turbine blades
Azevedo, C. R. F., & Sinátora, A. (2009) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2009.03.007 · Cited by 74
Research works printed but not linked: 14
- National Academies of Sciences Engineering and Medicine (2016). Commercial Aircraft Propulsion and Energy Systems Research: Reducing Global Carbon Emissions, The National Academies Press.
- Dewangan, 2015, Gas turbine blades—A critical review of failure at first and second stages, Int. J. Mech. Eng. Robot. Res., 4, 216
- Rao, 2014, Failure mechanisms in turbine blades of a gas turbine Engine—An overview, Int. J. Eng. Res. Dev., 10, 48
- Drury, C.G., and Gramopadhye, A.K. (1990, January 12–13). Training for visual inspection. Proceedings of the 3rd FAA Meeting on Human Factors in Aircraft Maintenance and Inspection: Training Issues, Atlantic City, NJ, USA.
- Waite, S. (2006, January 19–21). Defect Types and Inspection. Proceedings of the MIL17 Maintenance Workshop, Chicago, IL, USA.
- Chalimoniuk, 2015, Types of Damages to Turbines of Aircraft Turbine Engines; Diagnosing Capabilities, J. Pol. C., 4, 1
- Ilcewicz, L. (2006, January 19). Composite damage tolerance and maintenance safety issues. Proceedings of the FAA Damage Tolerance and Maintenance Workshop, Rosemont, IL, USA.
- Khan, M.A.U. (1998, January 26–29). Non-destructive Testing Applications in Commercial Aircraft Maintenance. Proceedings of the 7th European Conference on Non-destructive Testing, Copenhagen, Denmark.
- Iannone, L., and Rector, A. (2008). Calculations in OWL, University of Manchester. Available online: http://ceur-ws.org/Vol-432/owled2008eu_submission_17.pdf.
- Juengert, 2008, Damage Detection in Wind Turbine Blades using two Different Acoustic Techniques, J. Nondestruct. Test., 34, 11
- Hu, Y., and Kasabov, N. (2007). Ontology-Based Framework for Personalized Diagnosis and Prognosis of Cancer Based on Gene Expression Data, Springer.
- Taha, 2015, An Ontology-Based Expert System for General Practitioners to Diagnose Cardiovascular Diseases, Adv. Comput. Sci. Technol., 8, 53
- Thirugnanam, 2013, An Ontology Based System for Predicting Disease using SWRL Rules, Int. J. Comput. Sci. Bus. Inform., 7, 1
- Groover, M.P. (2012). Fundamentals of Modern Manufacturing: Materials, Processes, and Systems, Wiley. [5th ed.].
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Muthuraman, R. K., Manikandan, S. P., Manisha, C., Elavarasi, M., & Srividhya, P. K. (2026) · Innovative Infrastructure Solutions · doi:10.1007/s41062-026-02670-6
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Martinez-Guo, Z., Kirk, C., Mogalapalli, R., Maneatis, N., Dillard, T., & Kappes, M. (2026) · Wear · doi:10.1016/j.wear.2026.206834
Maalaoui, M., Westermann, H.-H., Kakouni, M. A., Hao, T., Niedermeyer, J., Oel, M., Meyer, I., & Lachmayer, R. (2026) · Procedia CIRP · doi:10.1016/j.procir.2026.05.333
Bakır, H., Demircioğlu, U., Hafez, A. H. A., Alzubi, R., & Albalawi, E. M. (2026) · Scientific Reports · doi:10.1038/s41598-026-59773-4
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Ahrens, V., Bath, L., & Schüppstuhl, T. (2026) · Springer eBooks · doi:10.1007/978-3-031-91463-8_30
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Metadata
- Authors
- Aust, J., & Pons, D.
- Affiliations
- University of Canterbury (NZ)
- Corresponding author
- Pons, Dirk
- Venue
- Aerospace
- Publisher
- MDPI AG
- Published
- 2019-05-21
- Volume
- 6
- Issue
- 5
- Pages
- 58
- ISSN
- 2226-4310 (ISSN-L)
- OpenAlex
- W2945187849
- Cited by
- 68
- References
- 40
- Citations by year
- 2026: 13 · 2025: 12 · 2024: 5 · 2023: 17 · 2022: 8 · 2021: 10 · 2020: 1 · 2019: 2
- FWCI
- 2.40
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Aerospace (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UC Research Repository (University of Canterbury) (submittedVersion, cc-by)
- Funders
- Christchurch Engine Centre (2019)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Technical Engine Diagnostics and Monitoring
- Physical Sciences › Engineering › Mechanical Engineering › Mechanical Failure Analysis and Simulation
- Physical Sciences › Engineering › Control and Systems Engineering › Aerospace Engineering and Applications
- Keywords
- gas turbine blades, defect classification, root cause analysis, surface damage, wear, material separation, material deformation, fatigue (material), impact damage
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