A New Prognostic Approach for Hydro-generator Stator Windings
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Abstract
Significant improvements in hydro-generator diagnostics were achieved, in the past decades, by using continuous online measurements and a number of periodic tests. In recent years, the diagnostic raw data has been converted into more useful information by way of integrated diagnostic systems that used expert knowledge. For example, an integrated methodology for hydro-generator diagnostics was developed at Hydro-Québec’ s research institute (IREQ) using a Web-based application. This comprehensive diagnostic system gives the degradation state of generator stator winding insulation by using a portfolio of diagnostic tools. Combining the results leads to a health index ranging from 1 (good condition) to 5 (worst condition). This system is used by Hydro-Québec’s power plant managers as well as technical support and maintenance engineers in the context of condition-based maintenance (CBM). The next step of development is to add new prognostic-related features. This involves automatic identification of active failure mechanisms, root cause analysis and estimation of the stage of advancement of any active mechanism. These characteristics form the basis of predictive maintenance and support the optimization of maintenance strategies.
The approach is based on a number of causal trees (the failure mechanisms) formed by the combination of sequential physical degradation states that ultimately lead to a failure mode. Each combination of sequential physical states is unique and defines a particular failure mechanism. Failure mechanism analysis was followed by identification of all symptoms (diagnostics measurements, observations) with their respective thresholds defining each physical state.
This paper presents the development of a prognostic approach where the modeling of failure mechanisms is combined with observable symptoms from our diagnostic system for the identification of active failure mechanisms.
How to Cite
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Prognostic, hydro-generator, stator winding, predictive maintenance, failure mechanisms, symptoms, physical state
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