Visual Monitoring of Industrial Operation States Based on Kernel Fisher Vector and Self-organizing Map Networks

As industrial process becomes increasingly complicated and the correlation between industrial process variables tends to exhibit strong nonlinear characteristics, how to effectively and visually monitor industrial operation states is challenging. A method based on kernel Fisher vector and self-organizing map networks (KFV-SOM) is proposed to improve the visualization of process monitoring. In KFV-SOM, kernel Fisher discriminant analysis is first employed to map data into high-dimensional space by using a nonlinear function, and the optimal Fisher feature vector, which can represent industrial operation states fittingly, is extracted. That is, the normal state and different kinds of faults can be distinguished well in the Fisher feature vector space. The topological structure of the Fisher feature vector space is then visualized intuitively on the two-dimensional output map of self-organizing map (SOM) with the Fisher feature vector as the input of the SOM network. Thus, the KFV-SOM can effectively realize the visualization of monitoring. Continuous stirred tank reactor process is applied to illustrate the capability of KFV-SOM. Result shows that KFV-SOM can effectively visualize monitoring, and it is better in showing the operation states of normal state and different kinds of faults on the output map of the SOM network than SOM, SOM integrated with principal component analysis, SOM integrated with correlative component analysis, SOM integrated with Fisher discriminant analysis, and SOM integrated with canonical variable analysis.