Diagnosis of cavitation in centrifugal pumps.

The thesis introduces an algorithmic framework for diagnosis of cavitation in centrifugal pumps, founded upon methods of artificial intelligence. The framework forms a blueprint for a wide range of on-line condition monitoring systems with cavitation diagnosis capability. It relies on information provided by a single acceleration sensor mounted on casing of the monitored pump. The signal provided by the sensor is evaluated by an artificial neural network classifier. Adaptation of the classifier to a specific pump type is carried out by means of a supervised learning algorithm. Furthermore, a complete cavitation diagnosis application methodology, developed by the author, is presented in the thesis. It deals with issues of implementation of cavitation diagnosis to a variety of centrifugal pumps employed in industrial environment.Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi