Abstract: Fault detection is an important technology to ensure the normal operation of modern industry. With the large-scale and complex production, data-driven fault detection has been widely used in the field of fault detection. The basic principle and the implementations of data-driven fault detection including the univariate statistical detection and the multivariate statistical detection are illustrated in this paper. Based on the basic theory of data-driven fault detection and the platform of MATLAB GUIDE, the design scheme and the implementation process of the visual detection toolbox are proposed. According to the features of the monitored data, the relevant detection method can be chosen in the designed toolbox to improve the fault detection ratio effectually. The fault detection has been simulated by the toolbox in the Tennessee-Eastman process simulation system. The results show that the univariate method can realize the fault detection and even the fault isolation. But the method may produce a lot of occupying space of the detection charts. Besides, compared with the univariate method, the multivariate method can get the detection results more directly with higher sensitivity.

Key words: data-driven, fault detection, MATLAB toolbox, visual interface