Master thesis in statistical modeling at Siemens

Job description

Siemens is looking for master students to do their thesis work in the area of ' Statistical Modeling' as a part of operation experience program

Siemens is a global powerhouse focusing on the areas of electrification, automation and digitalization. We’re offering a wide range of pioneering products for energy efficiency, industrial productivity, affordable healthcare and intelligent infrastructure, with a quickly growing focus on sustainability.

Siemens Industrial Turbomachinery AB (SIT AB) in Sweden is part of the Siemens Energy Sector. SIT AB delivers gas turbines, steam turbines, turn-key power plants, service and components for heat and power production. All under one roof – from research and development, manufacturing, marketing, sales and installation of turbines and complete power plants to service and refurbishing. There are today about 2 700 employees in Finspång.

Project Field Experience in SIT AB, a large amount of field experience data is continuously generated in form of various reports from maintenance events, component repair and operation history. These reports include detailed information about the turbine operation history as well as its condition and reported damages on individual components. This field experience data, although noisy, invariably portray environmental factors, measurement errors, and loading conditions, or in short, reality. By establishment of a process to collect and maintain this information in a database format, exploration and knowledge discovery using this data became a subject of high interest. This Master thesis is a part of efforts done to develop advanced visualization tools together with the proper sequence mining algorithms to discover the hidden relationships between different events and all the other affecting variables like loading, configuration and environmental parameters.

Project description:

Thesis nr.1 - Reliability prediction using multivariate operational time-series

Gas turbines are complex and expensive rotating equipment which should work with a high reliability. The operational parameters of each gas turbine is monitored and collected in the form of analogue and digital signals which is known as time-series data. The history of maintenance events and failures of the core components is also available for each engine. The goal of this project is to be able to find the patterns in operation that can describe the failures of the components. The result can be used to establish a prediction model that can predict the reliability factor for each installation based on the history of operation and desired operation profile in future. The project is suitable for 1 student with good statistical and modelling background. The student should have the good knowledge of signal processing, pattern mining and sequence mining algorithms. Student will work closely with domain experts.

Thesis nr.2 - Component state prediction based on field experience data

Within the rotating equipment, such as gas turbines, there are critical components like turbine blades which have limited life time based on hours and cycles. Due to criticality, these components are traced and their condition is evaluated regularly at the maintenance events. By knowing the consumed life in terms of hours and cycles until each of these condition assessments and using the complementary data from the sensors like the temperature, loading and speed, one should be able to develop some sort of statistical state prediction models to predict if the components can survive until their end of the life or not. This model can use a training dataset from thousands of previously assessed components which is available in a database. The project is suitable for 1 student with good statistical and modelling background. Student will work closely with domain experts.

How do I apply?

You are welcome to send in your application with resume and cover letter via www.siemens.se/ledigajobb , ref.nr. 245665, but no later than2017-01-23. The selection is ongoing.

Please refer by the number and/or the name of the thesis you are interested in above and why you are suitable for this thesis.

For any questions regarding the application process or the thesis, please contact Davood Naderi, Ph.D. davood.naderi@siemens.com , 072-57 22507.

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