Estimation of turbine runner blade measured strains from indirect measurements using machine learning

Hydro-Québec has data acquisition systems for a multitude of sensors, some of which have been installed since almost 20 years in its electrical generation equipment (turbine-generator units – TGU). The collected data is primarily used to ensure that the information is adequate in the event of an equipment breakdown or for specific behavioral studies. Data from monitoring systems are little used in routine maintenance management activities, often due to lack of time and adequate and/or effective analysis methods.
Equipment maintenance is an important part of Hydro-Québec’s equipment management activities. The creation and maintenance of a surveillance system is a major investment for the company. With the development of machine learning analysis approaches, the goal is to provide operators with a clearer view of real-time asset status and predictions about their potential for use.

Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

Institut de Recherche Hydro-Québec

Discipline:

Computer science

Sector:

Professional, scientific and technical services; Utilities

University:

Université de Montréal

Program: