Design and Development of Deep Learning-based Health Assessment System for Hydropower Units
The equipment of large hydroelectric units is complex,the coupling of fault causes is complex,and signal monitoring and analysis are difficult.Traditional maintenance methods rely on regular inspec-tions,empirical judgments,or simple linear evaluations,making it difficult to detect and accurately ana-lyze faults in a timely manner.The intelligent health assessment of hydroelectric units can achieve real-time monitoring and fault diagnosis of hydroelectric units,improve the pertinence of maintenance equip-ment,effectively reduce power generation costs,and enhance the competitiveness of power plants.This article develops a health monitoring function through feature value extraction and analysis,utilizing Gaussian models and statistical pattern recognition methods to achieve performance evaluation and quanti-fy health indicators.By evaluating historical data through testing and combining expert knowledge judg-ment,this system can effectively achieve fault diagnosis of hydroelectric units,with accuracy meeting usage requirements,solving the problem of difficult fault diagnosis of hydroelectric units,and providing safety assurance for the stable operation of hydroelectric units.
SPRimage recognitionhealth assessmentgaussian mixture modelcharacteristic value
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