A complex process fault prognosis approach based on multivariate delayed sequences

Complex process fault prognosis is a key scientific issue that ensures the security of the process and reliable operation, however complex systems work state is often determined by multivariate delayed sequence. It contains the relationship between the variables and the time delay information, so it has information completeness. So a complex process fault prognosis approach based on multivariate delayed sequences is proposed. First this method construct the Time Delay Signed Direct Digraph(TD-SDG) to get multi-delayed sequence, then combine Independent Component Analysis and ELM neural network to get the independent component of the multi-delayed sequence, and finally realize the purpose of fault prognosis of complex system. The simulation results on Tennessee Eastman process illustrate that the proposed method can predict fault earlier 15 min, increase operator's reaction time and detect the fault.