Key-performance-indicators-related fault subspace extraction for the reconstruction-based fault diagnosis

As a typical data-driven technology, projection to latent structure (PLS) has been successfully applied in the quality-related fault diagnosis. However, the oblique decomposition induced by PLS results in redundant component in fault subspace, which imposes a negative influence on the reconstruction-based fault diagnosis. Thus, two fault subspace methods are proposed, including nonlinear iterative partial least squares (NIPALS) fault subspace (N-FS) and improved PLS (IPLS) fault subspace (I-FS) extraction methods. For N-FS, the fault subspace is extracted by the nonlinear iteration, which captures variations of the output. For I-FS, through orthogonal decomposition by IPLS, the useless information is largely eliminated and a purer fault subspace is extracted by the novel iteration mode. A quality-related fault diagnosis strategy is designed, where the fault can be reconstructed by a lower dimensional fault subspace. Two case studies including simulation example and Tennessee Eastman process are conducted to validate the effectiveness of the proposed methods.