TY - BOOK

T1 - Automated data inspection in jet engines

AU - Hullait, Harjit

PY - 2020

Y1 - 2020

N2 - Rolls Royce accumulate a large amount of sensor data throughout the testing anddeployment of their engines. The availability of this rich source of data offers exciting opportunities to automate the monitoring and testing of the engines. In this thesis we have developed statistical models to make meaningful insights from engine test data. We have built a classification model to identify different types of engine runningin Pass-Off tests. The labels can be used for post-analysis and highlight problematic engine tests. The model has been applied to two different types of engines, in which it gives close to perfect classification accuracy. We have also created an unsupervised approach when there are no defined classes of engine running. These models have been incorporated into Rolls Royce systems.Early warnings for potential issues can enable relatively cheap maintenance tobe performed and reduce the risk of irreparable engine damage. We have therefore developed an outlier detection model to identify abnormal temperature behaviour. The capabilities of the model are shown theoretically and tested on experimental and real data.Lastly, in a test decisions are made by engineers to ensure the engine complieswith certain standards. To support the engineers we have developed a predictivemodel to identify segments of the engine test that should be retested. The model is tested against the current decision making of the engineers, and gives good predictive performance. The model highlights the possibility of automating the decision making process within a test.

AB - Rolls Royce accumulate a large amount of sensor data throughout the testing anddeployment of their engines. The availability of this rich source of data offers exciting opportunities to automate the monitoring and testing of the engines. In this thesis we have developed statistical models to make meaningful insights from engine test data. We have built a classification model to identify different types of engine runningin Pass-Off tests. The labels can be used for post-analysis and highlight problematic engine tests. The model has been applied to two different types of engines, in which it gives close to perfect classification accuracy. We have also created an unsupervised approach when there are no defined classes of engine running. These models have been incorporated into Rolls Royce systems.Early warnings for potential issues can enable relatively cheap maintenance tobe performed and reduce the risk of irreparable engine damage. We have therefore developed an outlier detection model to identify abnormal temperature behaviour. The capabilities of the model are shown theoretically and tested on experimental and real data.Lastly, in a test decisions are made by engineers to ensure the engine complieswith certain standards. To support the engineers we have developed a predictivemodel to identify segments of the engine test that should be retested. The model is tested against the current decision making of the engineers, and gives good predictive performance. The model highlights the possibility of automating the decision making process within a test.

U2 - 10.17635/lancaster/thesis/916

DO - 10.17635/lancaster/thesis/916

M3 - Doctoral Thesis

PB - Lancaster University

ER -