Abstract

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.

Highlights

  • Current research in the development of engine fault diagnostics methods have effectively advanced in several directions with the two most popular

  • To achieve the above-mentioned goals an effective diagnostic method based on data from the engine should form the bases for designing a diagnostic method

  • That is why Data Driven Fault Diagnosis Scheme based on statistical methods, machine learning, and statistical pattern recognition approaches are used as the basis for developing new advanced fault diagnostic system for aircraft engine health management

Read more

Summary

Научный Вестник МГТУ ГА Civil Aviation High Technologies

USING MODERN CLUSTERING TECHNIQUES FOR PARAMETRIC FAULT DIAGNOSTICS OF TURBOFAN ENGINES. 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. Key word: 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. The author expresses his gratitude to Prof. Chichkov of the Moscow State Technical University of Civil Aviation, Russia, for his guidance, support and assistance

INTRODUCTION
PROBLEM DESCRIPTION
CONCLUSION
ТУРБОВЕНТИЛЯТОРНЫХ ДВИГАТЕЛЕЙ
СПИСОК ЛИТЕРАТУРЫ
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call