Since system structures are more and more complex

it's hard to detect the faults only through physical and chemical mechanism model. In order to overcome these difficulties

a new method based on data-driven and complex network theory is proposed using partial correlation coefficient to determine adjacency matrix of variables

then define the network model. Based on the network topology structure

the characteristics of complex system network are calculated. According to the changing of parameters of network

it can be determined whether a system has failure by contrasting the diffidence between the fault model and normal model. At last

the proposed method is applied to TE process to test its validity.