Study On The Evaluation Method Of A Fan's Health Status

In the production of thermal power generation,the primary fan is the key auxiliary equipment to ensure the operation of the thermal power plant.Due to the complicated working environment of the primary fan,the fault is easy to occur,and the maintenance cost is high,the research on the health assessment technology of the primary fan equipment is of great significance to the economic and safe operation of the power plant.In this paper,the classification and identification of the operating conditions of a primary fan are carried out.Based on the field operation data of the primary fan,the multi-variable state estimation algorithm is used to study the corresponding equipment health assessment model.The main work has the following aspects:First,data preprocessing is performed.The historical data of various operating parameters of the primary fan of the thermal power plant were collected,and the field data was subjected to arithmetic processing such as noise reduction,compression,interpolation and complementation to obtain modeling data.After that,the data is dimension-reduced,and the main metadata with reasonable dimensions can be used to characterize the running state of the primary fan for identification of working conditions.Then,based on the basic principle of multivariate state estimation and its modeling method,the establishment of a wind turbine equipment health assessment model is established.Finally,aiming at the problem of high false positive rate of classical multivariable state estimation model,this paper proposes a method to establish memory matrix based on different working conditions,respectively,using fuzzy C clustering and improved K-means algorithm to operate the thermal power unit.The working condition is identified,and the memory matrix is established under each working condition to set the corresponding threshold.