Space expansion fault detection method based on dynamic inner total projection to latent structures
Dynamic inner partial least squares(DiPLS)is a dynamic extension algorithm based on the data-driven latent structure projection(PLS),which is used for dynamic feature extraction and key performance index prediction.In large equipment systems,the current moment samples collected by sensors are affected by historical samples,and may contain large noise.In the dynamic feature extraction,because the DiPLS algorithm does not extract the main components in descending order,there is still large variation in the residual space.It is difficult to effectively separate the dynamic and static information,which affects the fault detection performance.As such,a fault detection method based on the dynamic inner total PLS(DiTPLS)is proposed.Firstly,the dynamic internal partial least squares method and vector autoregressive(VAR)model are used to establish a dynamic model and detect fault,which is used to capture the quality-related dynamic information.An improved dynamic latent variable model(DLV)is established based on the structured dynamic principal component analysis(DPCA)algorithm for residual decomposition to extract the quality-independent dynamic and static information,and to construct appropriate statistics for fault detection.Numerical simulations and Tennessee-Eastman(TE)process experiments verify the effectiveness of the DiTPLS algorithm.
万方数据
维普
