A Fault Diagnosis Method for Boiler in Thermal Power Plants Based on Deep Learning
A deep learning based fault diagnosis method for thermal power plant boilers is proposed to address the issue of insufficient accuracy in current boiler fault diagnosis.On the basis of systematic analysis of boiler operation characteristics,important parameters are selected as boiler fault characteristic quantities.Design a fault feature classification model based on Deep Convolutional Neural Networks(DCNN)for deep learning and classification recognition of fault feature information.In response to the low efficiency and stability of DCNN parameter opti-mization,the Satin Bowerbird Optimization(SBO)algorithm improved by introducing the principle of avoidance is used to achieve adaptive optimization of DCNN parameters,Thus,a fault diagnosis model for thermal power plant boilers based on ISBO-DCNN is established.The ex-perimental results show that the proposed ISBO-DCNN model has higher diagnostic accuracy compared to other computational models,and can accurately diagnose faults in thermal power plant boilers.It has good engineering practical application ability in the operation and inspection work of thermal power plants.
thermal power plant boilersfault diagnosisdeep convolutional neural networkoptimization algorithm for satin blue bowerbirddeep learning
万方数据
维普
