Nonlinear sensor fault detection and data rebuilding based on principle component analysis

Fault detection method based on principle component analysis(PCA) which uses linear arithmetic is not appropriate for nonlinear systems because of inaccuracies in the fault mismatch detection and data rebuilding.An improved PCA arithmetic model is applicable to nonlinear plant thermal processes using local PCA model for various power loads based on the historical data.Then an appropriate PCA model was selected for each power load.Finally,data from adjacent model outputs was rebuilt through data fusion.The method improves the accuracy of fault detection and data rebuilding.Theoretical analyses and practical results show that the arithmetic can be accurately used for sensor fault detection and data rebuilding for a wide range of plant thermal processes.