Feedback

Faculté des Sciences appliquées
Faculté des Sciences appliquées
MASTER THESIS
VIEW 86 | DOWNLOAD 0

Master's Thesis : Data modelling of steam turbine performance

Download
Lamborelle, Maxime ULiège
Promotor(s) : Wehenkel, Louis ULiège
Date of defense : 25-Jun-2020/26-Jun-2020 • Permalink : http://hdl.handle.net/2268.2/9022
Details
Title : Master's Thesis : Data modelling of steam turbine performance
Author : Lamborelle, Maxime ULiège
Date of defense  : 25-Jun-2020/26-Jun-2020
Advisor(s) : Wehenkel, Louis ULiège
Committee's member(s) : Geurts, Pierre ULiège
Louppe, Gilles ULiège
Demarneffe, Renaud 
Language : English
Keywords : [fr] Anomaly Detection
[fr] Steam Turbine
[fr] Condition Based Maintenance
Discipline(s) : Engineering, computing & technology > Computer science
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master : ingénieur civil en science des données, à finalité spécialisée
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[fr] Maintaining industrial steam turbines is a very difficult task. It is now possible, using data- driven methods, to detect anomalies quickly to be able to fix them before the anomalies get bigger. In this project, a systematic methodology to detect anomalies on steam turbines is developed. Taking data coming from sensors placed in the turbine we can output the main anomalies as well as the main possible causes of the anomalies. Different algorithms are at the core of the anomaly detection process. One of them, based on Recurrent Neural Network, is a new architecture that can detect big anomalies as well as smaller anomalies. This methodology was developed on a first turbine and then applied to a different turbine proving that this newly developed methodology is systematic and only needs a few days to be adapted to a different case. The ability of this approach to successfully detect steam turbine anomalies and identify the most probable causes of these anomalies has been validated by experts.


File(s)

Document(s)

File
Access MasterThesis.pdf
Description:
Size: 14.47 MB
Format: Adobe PDF
File
Access MasterThesis_Summary.pdf
Description: One page Summary
Size: 80.72 kB
Format: Adobe PDF

Annexe(s)

File
Access Codes.zip
Description: Python scripts and notebooks
Size: 7.64 MB
Format: Unknown
File
Access Images_TFE.zip
Description: Representative illustrations
Size: 860.88 kB
Format: Unknown

Author

  • Lamborelle, Maxime ULiège Université de Liège > Master ingé. civ. sc. don. à . fin.

Promotor(s)

Committee's member(s)

  • Geurts, Pierre ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
    ORBi View his publications on ORBi
  • Louppe, Gilles ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data
    ORBi View his publications on ORBi
  • Demarneffe, Renaud
  • Total number of views 86
  • Total number of downloads 0










All documents available on MatheO are protected by copyright and subject to the usual rules for fair use.
The University of Liège does not guarantee the scientific quality of these students' works or the accuracy of all the information they contain.