基于深度学习的水电机组健康评估系统设计

Design and Development of Deep Learning-based Health Assessment System for Hydropower Units

大型水电机组装备复杂,故障原因耦合复杂,信号监测和分析困难。传统的检修手段依靠定期巡检、经验判断,或是简单线性评估,难以做到及时发现并准确分析故障所在。对水电机组的智能健康评估,可实现对水电机组的实时监测并进行故障诊断,由此可开展针对性的设备维修,降低运营成本,提升电厂竞争力。本文通过对特征值的提取和分析,利用Gaussian模型和统计模式识别等方法开发健康监测功能,实现水电机组的性能评价,并可对健康指标进行量化评估。通过测试对历史数据的评估,结合专家知识判断,本系统可有效实现对水电机组故障诊断,精确度满足使用要求,解决了水电机组故障诊断困难的难题,可为水电机组的稳定运行提供安全保证。

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 inspections,empirical judgments,or simple linear evaluations,making it difficult to detect and accurately analyze 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 equipment,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 quantify health indicators.By evaluating historical data through testing and combining expert knowledge judgment,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.