This study seeks to develop a scheme for fault detection, under the framework of multivariate process monitoring, employing the D-vine copula-based dependence measure (D-VCDM). This measure adapts to nonlinear and non-Gaussian dependence among high-dimensional variables. We improve upon the previous procedures by incorporating the quantile regression neural network and kernel density estimation. Additionally, we modify the conventional generalized local probability through a variable scale trick (VSGLP), to describe the evolution process of anomalies in the most fault-prone region more intuitively. The scheme is termed as D-VCDM-VSGLP, and its feasibility is verified through a numerical experiment and a real-world application on supervisory control and data acquisition system data collected from a wind turbine. We find that it outperforms the initial C-vine copula-based dependence description and multilinear principal component analysis in terms of accuracy. Besides, the adjustable parameters, including the number of intervals and the coefficient of scale variability in VSGLP, are designed to be convenient for practical use. (C) 2020 Elsevier Ltd. All rights reserved.