Association Rules Mining Based on Principal Component Analysis and Sensor Fault Detection of Power Plant

Similarity association rules data mining method based on principal component analysis(PCA) was proposed.Similarity association rules parameters was found through PCA and a least squares support vector regression(LS-SVR) model that detects sensor fault was built.Then the sensor fault location was implemented on the base of the reconstruction residuals from the LS-SVR model,which using the relationship of these similarity association rules parameters.Data reconstruction was implemented by the LS-SVR model instead of fault data.Data from a 300 MW unit were validated by the proposed method.The result reveals that the method can find high similarity association rules parameters fast and effectively.The LS-SVR Model can locate the sensor fault and get credible reconstruction data by using of the relationship of these similarity association rules parameters.