Gas Turbine Fault Detection Method Based on Control System
A gas turbine fault identification system is designed,which integrates expert knowledge within the control system to achieve real-time monitoring and fault prediction of gas turbine operation.Currently,fault diagnosis in gas turbines primarily relies on control system alarms and the experience of maintenance personnel,coupled with post-analysis,which has limitations in maintenance efficiency.To enhance efficiency,a novel approach is proposed in this research.Starting from clearly defined fault diagnosis objectives,the method analyzes historical operating data under different operating conditions to construct a parameter historical dataset.Utilizing visualization tools for analysis,the approach trains fault warning models based on the analysis results,formulates fault judgment and traceability rules,and deploys them online to the control system.Through gas turbine simulation verification,it shows good timeliness and accuracy,indicating its potential for application in actual operational environments.
fault diagnosisfault predictioncontrol systemmachine learningexpert system
万方数据
维普
