Using modern clustering techniques for parametric fault diagnostics of turbofan engines

The 21st century aviation and aerospace technologies have evolved and become more complex and technical. Turbofan jet engines as well as their cousins, the rocket engines (liquid/solid) have gone through several design upgrades and enhancements during the course of their design and exploitation. These technological upgrades have made engines very complex and expensive machines which need constant monitoring during their working phase. As the demand and use of such engines is growing steadily, both in the civilian and military sectors, it becomes necessary to monitor and predict the behavior of parametric data generated by these complex engines during their working phases. In this paper flight parameters such as Exhaust Gas Temperature (EGT), Engine Fan Speeds (N1 and N2), Fuel Flow (FF), Oil Temperature (OT), Oil Pressure (OP), Vibration and others where used to determine engine fault. All turbo fan engines go through several distinctly different working phases: Take-off phase, Cruise phase and Landing phase. Recording generated parametric data during these different phases leads to a massive amount of in-flight data and maintenance reports, which makes the task of designing and developing a fault diagnostic system highly challenging. It becomes imperative to use modern techniques in data analysis that can handle large volumes of generated data and provide clear visual results for determining the technical status of the engine under investigation/monitoring. These modern techniques should be able to give clear and objective assessment of the object under investigation. Cluster analysis methods based on Neural Networks such as c-means, k-means, self-organizing maps and DBSCAN algorithm have been used to build clusters. Differences in cluster groupings/patterns between healthy engine and engine with degraded performance are compared and used as the bases for defining faults. Fault diagnosis plays a crucial role in aircraft engine management. Timely and accurate detection of faults is the foundation on which maintenance turnaround times, operational costs and flight safety are based. The data used in this paper for analysis was obtained from flight data recorder during one flight cycle. The final decision on a fault is taken by an engineer.

engine fault diagnostics, parametric data, turbofan jet engines, monitoring, in-flight data handling, neural network, cmeans, k-means, dbscan, clustering analysis, cluster pattern, clustering techniques, algorithm, flight parameters, exhaust gas temperature, data analysis, self-organizing maps

1. Buraimah, I.J. (2015). Primenimosti neyrosetevykh algoritm k otsenke tekhnicheskogo sostoyaniya aviatsionnykh dvigateley [Application of the neural network algorithm for determining technical condition of aircraft engine]. Nauchnyye chteniya po aviatsii, posvyashchennyye pamyati N.Ye. Zhukovskogo: sbornik dokladov XII Vserossiyskoy nauchno-tekhnicheskoy konferentsii [12th Russian Scientific and Technical Conference, (technical reading on aviation), Dedicated to the memory of Ghukovski, N.E.], pp. 104–106. (in Russian).

2. DePold, H.R. and Gass, F.D. (1999). The application of expert systems and neural networks to gas turbine prognostics and diagnostics. ASME. Journal of Engineering for Gas Turbines and Power, vol. 121, issue 4, pp. 607–612. DOI: https://doi.org/10.1115/1.2818515

3. Polycarpou, M.M. (1994). An on-line approximation approach to fault monitoring, diagnosis, and accommodation. SAE Technical Paper 941217. DOI: https://doi.org/10.4271/941217

4. Merrigton, G.L. (1994). Fault diagnosis in gas turbines using a model-based technique. ASME Journal of Engineering for Gas Turbines and Power, vol. 116, issue 2, pp. 374–380. DOI: https://doi.org/10.1115/1.2906830

5. Gorinevsky, D., Nwadiogbu, E. and Mylaraswany, D. (2002). Model based diagnostics for small-scale turbomachines. Proceedings of the 41st IEEE Conference on Decision and Control, Las Vegas, NV, USA, vol. 4, pp. 4784–4789. DOI: 10.1109/CDC.2002.1185136

6. Buraimah, I.J. (2014). O primenimosti neyrosetevyye algoritm k otsenke tekhnicheskogo sostoyaniya aviatsionnykh dvigateley [Using neural network based algorithm to determine technical condition of aircraft engines]. Aviatsiya: istoriya, sovremennost, perspektivy razvitiya: sbornik trudov IV Mezhdunarodnoy nauchno-prakticheskoy konferentsii [4th International Technical and Scientific Conference, ‘Aviation: history, current technology, perspective development’]. Minsk, Belarus. (in Russian)

7. Wang, X. and Syrmos, V.L. (2008). Fault detection, identification and estimation in the electro-hydraulic actuator system using EKF-based multiple-model estimation. 16th Mediterranean Conference on Control and Automation, pp. 1693–1698. DOI: 10.1109/MED.2008.4602248

8. Luo, J., Namburu, M., Pattipati, K., Qiao, L., Kawamoto, M. and Chigusa, S. (2003). Model-based prognostic techniques [maintenance applications]. Proceedings AUTOTESTCON 2003. IEEE Systems Readiness Technology Conference, Anaheim, CA, USA, pp. 330–340. DOI: 10.1109/AUTEST.2003.1243596

9. Ofsthum, S.C. and Wilmering, T.J. (2004). Model-driven development of integrated health management architectures. IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720). Big Sky, MT, vol. 6, pp. 3692–3705. DOI: 10.1109/AERO.2004.1368185

10. Bezdek, J.C. (1981). Pattern recognition with fuzzy objectives function algorithm. Springer US, New York, 272 p. DOI: 10.1007/978-1-4757-0450-1

11. Dunn, J.C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact, wellseparated clusters. Cybernetics and System Journal, vol. 3, issue 3, pp. 32–57. DOI: https://doi.org/10.1080/01969727308546046