Machine Health Monitoring and Prognostication Via Vibration Information

Health monitoring of the hydropower turbine and timely identification of potential failure areas can prevent failure of the entire vertical axis hydropower turbine. A health monitoring system integrated within the turbine could locate blade failures, reducing hydropower turbine life-cycle costs and the costs of energy. System health management is a concerted effort with applicability to machinery maintenance operations and support. Condition based maintenance (CBM) seeks to generate a design for a machinery CMS system that performs diagnoses and failure prediction on hydropower turbine. Eventually, a variety of monitoring systems will be instrumented with embedded high-performance processors to monitor equipment performance, diagnosis failures, and predict anticipated failures. The purpose of this paper is to discuss an approach to integrate data collection and analysis of hydropower turbine for the purpose of assessing equipment condition and maintaining their operational performance to support a system wide CBM concept in a cost effective way