Abstract: A fault detection method based on dual control strategy was proposed to address the issue of incomplete statistics of variable fault information and poor fault detection performance in complex industrial processes. The input data was standardized and the deviation variables were obtained to reveal fault information and achieve first level control. The deviation variables were processed to generate new auxiliary monitoring statistics, achieving secondary control. A parameter adaptive method based on feedback adjustment was adopted to set the threshold for the difficulty in determining the threshold of auxiliary monitoring statistics. The proposed method was used to detect faults of the Tennessee Eastman process (TE process) , and was compared and validated with the fault detection methods of the improved Euclidean distance control (IEDC) and traditional principal component analysis (PCA) method. The results show that compared with the IEDC and PCA methods, the proposed method can monitor more fault information of variables in the TE process, and has a higher fault detection rate and lower false alarm rate in fault detection of multiple types, which can be effectively applied to complex industrial processes.