Model-Based Vibration Condition Monitoring for Fault Detection and Diagnostics in Large Hydrogenerators

Abstract

Large hydrogenerators are rotating machines of vertical assembly, equipped with tilting pad journal bearings, operating at subcritical speeds (80 to 200 rpm). The nominal power of these machines may reach 700 MW, what makes vibration-based condition monitoring a compulsory task. Several international standards are applicable to vibration monitoring of hydrogenerators. However, their condition assessment criteria are grounded on measurements performed on a wide set of hydrogenerators, with power varying from 1 to 700 MW and speed in the range 60 to 1800 rpm. As result, some limits are excessively tight while others are too much permissive. Moreover, experimental observations revealed that the dynamic behavior of these machines might present significant short and long-term changes, many times with no apparent reasons. Part of this behavior is due to the lack of a defined radial static load in the journal bearings, as well as to external agents, like generator electromagnetic field or seasonal variations of bearing cooling water temperature. All these aspects make difficult the using of statistical pattern recognition. It is necessary a better understanding of the influencing mechanisms of the vibratory behavior of these machines, to differentiate normal changes from those originated by incipient faults. This paper proposes the using of a model-based approach to overcome these problems and exemplifies this using on a set of 700 MW hydrogenerators. The results obtained indicated that even simplified models might present satisfactory results, especially when models performance are improved using additional information collected by the monitoring system.