A Data-driven Fault Detection Method Based on Dissipative Trajectories

Fault detection is becoming increasingly important as the complexity of industrial process develops. In this paper, a data-driven fault detection method is proposed. The dissipativity theory is adopted to find the appropriate dissipativity properties for the process input output trajectory. The dissipativity properties can be viewed as an Abstract energy property, and the dissipativity properties of input output trajectories represent process dynamic features. As faults occur, the dissipative trajectories will change thus allow fault detection to be performed based on these dissipativity properties. A training algorithm is developed to search for the related properties using input output data. A prior knowledge of the process can be incorporated into the algorithm to facilitate the training. The proposed fault detection method is illustrated on a case study of a mono-chlorobenzene plant simulated using VMGSim.