Big Engines, Small Outliers
Already in her diploma thesis Doris Schadler, BSc, worked on the identification of measuring errors of engine test beds. Since 2016 Schadler contributes decisively to the timely recognition of outliers in the data measured at the test beds. According to Schadler tests at the test beds are associated with great time expenditure as well as financial expenses, therefore making it important to recognize errors as soon as possible. These errors occur for example is a sensor is dirty or breaks. Schadler developed a statistical model for the software LEC MCheck which should identify implausible data points in real time. The starting point is an errorless data set, which serves as a basis for predicting future values. These are then compared with the real measured test results. The high complexity lies in the fact that the systems needs to be able to report gross deviations in real time for up to 500 data points per second.
I am fascinated by the interdisciplinary approach, the combination of mathematics with mechanical engineering and software engineering. -Doris Schadler
Read the article here.
Große Motoren, kleine Ausreißer
Der Standard, 30.08.2017
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Der Standard online, 05.09.2017