Artificial Neural Networks for Gas Turbine Monitoring

Research output: ThesisDoctoral Thesis (compilation)


Due to the deregulation of the electricity market the power producers are
forced to continuously investigate various means of maintaining/increasing
their profits. Improving the electrical efficiency through hardware upgrades is probably the most commonly employed measure, although the interest for enhancements with regard to plant operation is on the rise.

Plant operation improvement is often measured in RAM (reliability, availability and maintainability) which acts as an indication of how well a plant can be utilized.

The availability can be increased by employing various monitoring tools
allowing the maintenance to be based on the condition rather than equivalent operating hours, thereby extending the periods between overhauls. The reliability can also be increased by employing a combination of monitoring tools alerting the plant operators before faults are fully developed.

Modern power plants are equipped with distributed control systems delivering data to the control room through a considerable number of sensors. This data enables the development of data-driven methods for tasks such as condition monitoring, diagnosis and sensor validation. Artificial neural networks have proven suitable for the non-linear modeling of power plants and its components, and represent the data modeling tools used in this research.

Some of the results of the case studies are very accurate ANN models for
different types of gas turbines. Furthermore, the integration of these models and the development of user interfaces for online condition monitoring have been demonstrated.


  • Magnus Fast
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Energy Engineering
Original languageSwedish
Awarding Institution
Supervisors/Assistant supervisor
Award date2010 Dec 17
Print ISBNs978-91-7473-035-7
Publication statusPublished - 2010
Publication categoryResearch

Bibliographic note

Defence details Date: 2010-12-17 Time: 10:15 Place: M building, M:B External reviewer(s) Name: Dahlquist, Erik Title: prof Affiliation: Mälardalens högskola ---