Research on fault prognostics and warning for steam power auxiliary systems based on LSTM and threshold method
In order to enhance the operational safety and reliability of marine steam power auxiliary systems,and to re-duce ship downtime and potential safety incidents caused by system failures,a fault warning method combining Long Short-Term Memory(LSTM)networks and threshold methods is proposed.Initially,the LSTM model is utilized to process the his-torical operation data of marine steam power auxiliary systems,learning the system's dynamic behaviors and potential fail-ure patterns.Subsequently,by setting threshold methods in conjunction with the predictive outputs of the LSTM model,real-time monitoring and fault warning of the system state are achieved.Finally,fault warning experiments are conducted using a steam power auxiliary system model developed on the MINIS integrated simulation platform,taking the example of a steam turbine feedwater pump speed fault,to verify the effectiveness of the method.The experimental results demonstrate that the warning model,integrating the predictive capabilities of LSTM with the decision-making efficiency of the threshold method,can effectively identify and warn against speed faults of the steam turbine feedwater pump.By comparing with actual fault data,the model exhibits excellent performance in terms of prediction accuracy and timeliness of warnings.This method not only improves the accuracy of fault warnings but also provides robust decision support for marine maintenance and safety management.Furthermore,it offers a novel research perspective for fault warning in similar industrial systems.
steam power auxiliary systemfault prognostics and warninglong short-term memory network(LSTM)threshold method
万方数据
维普
