In response to the lack of state relationship analysis and poor functional scalability of the fault diagnosis system in the pump station monitoring system,this paper studies and implements a fault diagnosis system that can automatically construct diagnostic algorithms.In the field of fault diagnosis,samples are prone to imbalanced issues.To address this issue,explore and study methods for sample balance testing,algorithm training under sample imbalance,and effectiveness verification.By studying automated machine learning based on genetic algorithms to achieve this function,the system can easily expand and update its functions.
维普
万方数据
