The stiction of control valve is one of the main reasons for an industrial process control loop oscillation and valve nonlinear behavior, which not only reduces control loop performance and shortens valve service life but also may increase product quality fluctuations and energy consumption. Therefore, real-time detection of control valve stiction is important to ensure process operation stability. The control valve input signal OP (Output Position) and the process variable PV (Process Variables) waveforms are analyzed by the time-domain based traditional stiction detection method and the stiction index is calculated to determine whether there is stiction or not. However, in real industrial process, the process variable PV presents nonlinear and non-Gaussian random fluctuation characteristics, and the control valve stiction fault detection method based on time-domain waveform analysis has limitations in accuracy and generalization ability. Aiming at the random fluctuation characteristics of the process signal of the industrial process, a control valve stiction fault detection method based on Volterra model and kernel entropy component analysis (KECA) is proposed in this work. AVI-based KECA refers to as improved kernel entropy component analysis (IKECA). Firstly, nonlinear sticking control valves are modeled by the second-order Volterra model. Secondly, the OP-PV phase shift frequency-domain features are used to characterize stiction and extracted by the spectral analysis theory with the second-order Volterra series model. Then, for both faulty and normal data, the KECA method is used for dimensionality reduction, feature extraction and classification of multidimensional features, the angle variance index (AVI) is introduced as a statistic index and the kernel density estimation (KDE) is applied to determine the detection control limit AVILim to realize the real-time detection of control valve stiction fault. Industrial application software has been developed with the proposed method, and the industrial application results have verified the feasibility and effectiveness of the proposed method.