Defects in combustion chambers of aircraft engines might have an impact on the reliability of the downstream turbine and the machine’s performance. Detecting failures in the combustion chamber of an aircraft engine during operation may improve the resource management and the availability of the system. Aim of the ongoing research project is to find an approach to evaluate the state of the jet engine by analyzing the temperature and emissions field in the exhaust jet. This investigation is part of the collaborative research center SFB 871. The SFB 871 deals with the improvement of the regeneration process of complex capital goods such as aircraft engines. Maintenance, repair, and overhaul processes would be more efficient if the internal status of the engine would be known while still on the wing before it is disassembled. The feasibility of this approach is investigated for a pilot scaled model combustor, which provides optical access and allows the selection of “defined errors” in the combustor. It consists of an atmospheric tubular combustor with an array of eight premixed swirl burners with a maximum output of 160 kW. The operating conditions of one of the eight burners concerning power and air-fuel ratio, can be controlled. A power distribution between the burners is typical fault in an aircraft combustor and will be investigated in this study. It is observed that it is possible to determine small deviations by measuring density profiles applying a tomographic background-oriented schlieren (BOS) technique behind the combustor. Additionally, particle image velocimetry is used to measure differences in the velocity field of the exhaust gases. This study shows that a minimum power deviation of one burner in an array of a total of eight burners is detectable in the exhaust plane with the above mentioned measurement techniques.

The maintenance, repair, and overhaul process of aircraft engines might be optimised to reduce long and expensive downtime. At present, only limited forecasts of the engine condition can be made prior to disassembly. Since faulty conditions inside an engine affect the internal flow and spread out minimally through the turbine, a new methodological approach is to analyse the exhaust jet in order to detect anomalies. Failures lead to streaks of temperature or concentration through the combustion chamber, the turbine and downstream to the exhaust jet and, thus, can be observed with suitable measurement techniques. In the current work the influence of possible damages inside an aircraft engine on the temperature and velocity pattern at the exit of the combustion chamber is investigated in detail applying three-dimensional computational methods (CFD) to simulate the flow and reaction processes. It can be shown that a burner with reduced load leads to a clear influence on the exit pattern; whereas damages in the outer or inner liner, i.e. a mismatch of the secondary air flow, do not significantly affect the exhaust jet. Future studies focus on quantifying also the mixing processes in the subsequent turbine passage and developing approaches for the measurement of structures in the exhaust jet.

Developing AI systems for automatic train operation (ATO) requires developers to have a deep understanding of the human tasks they are trying to replace. This paper fills this gap and translates the regulatory requirements from the context of German railways for the AI developer community. As a result, tasks such as train’s path monitoring for collision prediction, signal detection, door operation, etc. are identified. Based on this analysis, a functionally justified sensor setup with detailed configuration requirements is presented. This setup was also evaluated by a survey within the railway industry. The evaluated sensors include RGB/IR cameras, LIDARs, radars and ultrasonic sensors. Calculations and estimates for the evaluated sensors are presented graphically and included in this paper. However, the ultimate sensor setup is still a subject of research. The results of this paper also address the lack of training and test datasets for railway AI systems. It is proposed to acquire research datasets that will allow the training of domain adaptation algorithms to transform other datasets, thus increasing the number of available datasets. The sensor setup is also recommended for such research datasets.

A disturbed combustion process in an aircraft engine has an impact on the internal flow and leads to specific irregularities in the species distribution in the exhaust jet. Measuring this distribution provides information about the combustion state and offers the possibility to reduce the engine down-time during inspection. The approach has the potential to improve the resource management as well as the availability and safety of the system. Aim of the research project is to evaluate the state of an aircraft engine by analyzing the emission field in the exhaust jet and using a support vector machine (SVM) algorithm for automatic defect detection and allocation.

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