Nonlinear principal component analysis (NLPCA) fault detection method achieves good detection results especially in a nonlinear process. Signed directed graph (SDG) model is based on deep-going information, which excels in fault interpretation. In this work, an NLPCA-SDG fault diagnosis method was proposed. SDG model was used to interpret the residual contributions produced by NLPCA. This method could overcome the shortcomings of traditional principal component analysis (PCA) method in fault detection of a nonlinear process and the shortcomings of traditional SDG method in single variable statistics in discriminating node conditions and threshold values. The application to a distillation unit of a petrochemical plant illustrated its validity in nonlinear process fault diagnosis.