Relative Principal Component Analysis Algorithm and Its Application in Fault Detection
Hongqiong Huang · Jisuanji fangzhen · 2007
After dataare standardized by traditional Principal Component Analysis (PCA), the processed data often show “Rotundity Distributing”. Therefore, it is difficult to choose representative principal components for fault detection and diagnosis. The concept of Relative Principal Component (RPC ) was proposed, and a new algorithm was given based on Relative Principal Component Analysis (RPCA). Some new concepts such as Relative Transform, Rotundity Distributing and so on were proposed. The new algorithm could overcome some disadvantages of Principal Component Analysis (PCA) for fault detection and diagnosis when data was Rotundity Distributing. The Relative Principal Components selected by RPCA are more representative, and their significance of geometry is more notable. The simulation demonstrates the effectiveness of the algorithm proposed.