Data-driven Model Based Process Alarm Event Prognosis

It is acknowledged that process industries involve a large amount offlammable and toxic materials. Any accidents could result in seriousenvironmental issues as well as great damage to human life and property.Hence, increasing attention has been paid to process safety ever since.Alternatively, vast amounts of industrial data recorded in the databases ofthe distributed control systems (DSC) and emergency shutdown (ESD)systems still remain "under-utilized", which are considered valuable toprocess safety concerns. Motivated by these observations, a noveldata-driven approach to process alarm event prognosis is introduced inthis paper. Therein, Logistic regression models and time series models areemployed to forecast alarm states and duration series, respectively.Consequently, a combination of the two predictions contributes to analarm event prognosis.Initially, the forecasting methods of Logistic regression models andtime series models are introduced. Based on characteristics of processalarm events, a two-tuple element is employed to describe an alarmevent, giving rise to a definition for the sequence of process alarm events. It's worth mentioning that both discrete-state and continuous-timecomponents are included in the sequence. Based on Logistic regressionand time series models, an approach to process alarm event prognosis isintroduced along with a detailed forecasting procedure. Finally, theproposed methods are applied to Tennessee-Eastman (TE) process andan industrial DMF recovery process respectively. Experimental resultsshow that a satisfactory prediction of the process alarm events can beachieved. Additionally, oriented to practical applications, a simpleprocess alarm management system is developed in VB language, whereOPC technology and SQL database are employed.The current work reveals that by means of the proposed methoduseful information can be extracted from the industrial data and anaccurate prediction of process alarm events can be made. Theexperimental results obtained are in good accordance with anticipation,illustrating feasibility and effectiveness as well as future applicationperspectives of the proposed methods.