Fault Diagnosis Method Based on Vibration Trend to Discriminate Cloud Model
In the field of turbine fault diagnosis,the variation trend of series data can reflect the operating state and development trend in the vibration process,which is the characteristic basis often used by experts in diagnosis.Due to the diversity and scarcity of fault samples caused by the structure and working condition of the turbine unit,as well as the relatively rich experience of ex-perts and qualitative description of the diagnosis status,a cloud model-based method for identifying the trend of turbine vibra-tion time series is proposed.By summarizing expert experience and fault cases,the cloud parameter evaluation model of qualita-tive trend is generated in combination with the uncertain cloud model.A large number of cloud drops are generated by cloud pa-rameters obtained by reverse cloud based on sample data,and the trend level determination is calculated by substituting the cloud parameter evaluation model.The trend discriminant decision tree is introduced to obtain the qualitative description of series data.Finally,the feasibility and effectiveness of the method are verified by taking a subcritical double-exhaust condensing steam tur-bine as the research object.
NETL
NSTL
万方数据
维普
