Fault Diagnosis of Centrifugal Fan Based on Grid Search Optimized Voting Weighted KNN

Abstract

Fan plays the roles of the forced draft fan, induced draft fan, primary air fan, seal fan, and powder exhauster. As an important auxiliary of the fossil-fuel power station, its working environment is harsh. Timely and efficient fault diagnosis can effectively reduce equipment failure and shutdown losses, and improve the efficiency of thermal power generation. K-Nearest Neighbour (KNN) has good classification ability for non-stationary data samples. In response to the shortcomings of the traditional KNN algorithm, this paper constructs a fault diagnosis model based on the voting weighted k-nearest neighbor algorithm. The model constructs a weight voting equation that is negatively correlated with the distance value based on the first k-nearest neighbors and then conducts fault diagnosis based on the voting score. We use grid search to optimize the model and select the k value in the model, and the relationship between the k value and accuracy was verified. The grid search optimization voting weighted k-nearest neighbor is used to diagnose the faults of nine common operating states of centrifugal fans, and the diagnostic accuracy can reach 100%.