Process monitoring technology is an effective means to guarantee operation safety and product quality of modern industrial processes. Most of traditional process monitoring methods extract data features by dimensionality reduction and require process data obeying Gaussian distribution

linearity and other conditions. Therefore

traditional methods cannot obtain preferable detection results for faults occurred under complex operating conditions. A mixture of D-vine copulas model was proposed for fault detection. First

complex correlation among process variables were directly extracted without dimensionality reduction and a statistical model of process variables was established to accurately describing nonlinear and non-Gaussian processes. Then

model parameters were optimized by expectation maximization (EM) algorithm and maximum pseudo-likelihood estimation. Finally

a generalized Bayesian inference-based probability (GBIP) index was constructed for real-time monitoring by optimized model parameters as well as theories of the highest density region (HDR) and density quantile. Application of the proposed mixture model to a numerical example and the Tennessee Eastman (TE) benchmark process illustrated effectiveness and performance in fault detection.