Fault diagnosis of centrifugal fans based on voting weighted GS-KNN
As a critical auxiliary component in thermal power generation,the efficient and timely diagnosis of faults in fans can significantly reduce downtime losses and enhance the overall efficiency of thermal power generation.The k-nearest neighbors(KNN)algorithm demonstrates strong classification capabilities for non-stationary data samples.To address the limitations of traditional KNN algorithms,this study proposes a vote weighted grid search k-nearest neighbors algorithm(vote weighted GS-KNN)for fault diagnosis.The algorithm utilizes grid search to select the optimal k value,establishes a weighted voting formula based on the negative correlation between distance values and the proximity of the top k neighbors,and performs fault diagnosis according to the voting scores.The vote weighted GS-KNN model is applied to diagnose nine common operational states of centrifugal fans,and the relationship between the fitted k values and diagnostic accuracy is explored.The diagnostic accuracy of the proposed model reaches 100%.
fault diagnosisthermal power generationgrid searchk-nearest neighbor algorithmvote weighting
万方数据
维普
