Application of the Enhanced Dynamic Causal Digraph Method on a Three-layer Board Machine

1. Application of the Enhanced Dynamic Causal Digraph Method on a Three-layer Board Machine Cheng Hui, Vesa-Matti Tikkala, Sirkka-Liisa Jämsä-Jounela Helsinki University of Technology Faculty of Chemical Technology and Material Sciences Research group of Process Control and Automation Nordic Process Control Workshop, Porsgrunn, Norway

2. Table of Contents • Introduction • Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method • Application of the EDCDG on an Industrial Process • Description of the Process and the Test Environment • Fault Diagnosis Results • Conclusions Nordic Process Control Workshop, Porsgrunn, Norway

3. 1. Introduction 1.1 Background • Fault diagnosis methods are needed in the paper industry due to complex processes and problems caused by the process faults • Fault diagnosis based on causal digraph methods has been researched since 1970’s Nordic Process Control Workshop, Porsgrunn, Norway

4. 1. Introduction 1.2 Aim of the Research • The aim of this study was to test the enhanced dynamic causal digraph method on a board machine simulator by simulating real industrial fault scenarios • Four fault scenarios were selected, one is presented in here Nordic Process Control Workshop, Porsgrunn, Norway

5. Table of Contents • Introduction • Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method • Application of the EDCDG on an Industrial Process • Description of the Process and the Test Environment • Fault Diagnosis Results • Conclusions Nordic Process Control Workshop, Porsgrunn, Norway

6. 2. Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method 2.1 Fault Detection and Isolation • Fault diagnosis using EDCDG method is based on the process models and the causal structure of the process • Method performs the detection by observing residuals generated by comparing model outputs and process measurements • The isolation is carried out by an inference mechanism which evaluates the residuals and forms a propagation path of the fault effect according to the causal structure Nordic Process Control Workshop, Porsgrunn, Norway

7. Cause-effect model Cause-effect model Pump rotation speed Tank level Mass flow rate Valve opening 2. Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method 2.2 Dynamic causal digraph model • Describes the cause-effect relationships between the process variables • The structure of the causal digraph is based on the prevailing causal dependencies in the process • The cause-effect models can be described by any mathematical model type Nordic Process Control Workshop, Porsgrunn, Norway

8. 2. Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method 2.3 Fault Detection • Faults are detected by observing the global residuals generated by the comparison of measurements and simulation values • Cumulative sum algorithm is used for detection Nordic Process Control Workshop, Porsgrunn, Norway

9. 2. Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method 2.4 Fault Isolation • The origin of the fault can be located by a recursive evaluation of local residuals • Total local residual (TLR) • Individual local residuals (ILR) • Multiple local residuals (MLR) • The nature of the fault can be also inferred from the residuals Nordic Process Control Workshop, Porsgrunn, Norway

10. 2. Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method • In case of a process fault a further step can be taken in order to isolate the actual process component causing the fault • There are 2n-1, where n is number of input variables, possibilities for the cause • A solution is to introduce process knowledge in a form of a matrix M • It describes the relationships between the arcs of the digraph Nordic Process Control Workshop, Porsgrunn, Norway

11. suspected arcs sets knowledge matrix inference decreased number of suspected arcs sets 2. Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method • The consistency between the suspected sets of arcs and process knowledge is checked Nordic Process Control Workshop, Porsgrunn, Norway

12. Table of Contents • Introduction • Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method • Application of the EDCDG on an Industrial Process • Description of the Process and the Test Environment • Fault Diagnosis Results • Conclusions Nordic Process Control Workshop, Porsgrunn, Norway

13. 3. Application of the EDCDG method on an Industrial Process • A procedure for offline testing: • Process study • Causal digraph modeling • Fault simulations/data collection • Fault diagnosis testing Nordic Process Control Workshop, Porsgrunn, Norway

14. Table of Contents • Introduction • Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method • Application of the EDCDG on an Industrial Process • Description of the Process and the Test Environment • Fault Diagnosis Results • Conclusions Nordic Process Control Workshop, Porsgrunn, Norway

15. 4. Description of the Process and the Test Environment • The process comprises a three-layer board machine, which has two stock preparation parts and three short circulations • The process was simulated in APROS simulation environment Nordic Process Control Workshop, Porsgrunn, Norway

16. Table of Contents • Introduction • Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method • Application of the EDCDG on an Industrial Process • Description of the Process and the Test Environment • Fault Diagnosis Results • Conclusions Nordic Process Control Workshop, Porsgrunn, Norway

17. 5. Fault Diagnosis Results 5.1 The causal digraph model of the process • A causal digraph model of the board machine was constructed • 55 variables in total • To describe to cause-effect relationships various model types were used • First principles models • Static regression • Neural networks • Also the knowledge matrix M was obtained Nordic Process Control Workshop, Porsgrunn, Norway

18. 5. Fault Diagnosis Results 5.2 Hydrocyclone plugging • Hydrocyclones are used for cleaning of the stock • In some cases they are vulnerable to plugging • A fault in the hydrocyclone cleaning plant causes • Decreased cleaning efficiency • Extra pressure loss Nordic Process Control Workshop, Porsgrunn, Norway

19. 5. Fault Diagnosis Results • The fault was simulated with the board machine model in two operation points • Global residuals were generated based on the simulation values and the measurements • 5 detected GRs Nordic Process Control Workshop, Porsgrunn, Norway

20. 5. Fault Diagnosis Results • Local residuals (TLR, ILR, MLR) were generated for the detected variables • The fault origin was located: total of three origins • acceptcon2 • headcon2 • headflow22 • Fault was identified as a process fault Nordic Process Control Workshop, Porsgrunn, Norway

21. 5. Fault Diagnosis Results • Three nodes with 4, 3 and 2 input arcs, respectively, resulted as: sets of suspected arcs • Process knowledge matrix M was used to decrease the number of them • Each set was tested by • If the above equation holds, the set can be accepted as a possible result • The number of possible results decreased to 5 Nordic Process Control Workshop, Porsgrunn, Norway

22. 5. Fault Diagnosis Results Nordic Process Control Workshop, Porsgrunn, Norway

23. Table of Contents • Introduction • Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method • Application of the EDCDG on an Industrial Process • Description of the Process and the Test Environment • Fault Diagnosis Results • Conclusions Nordic Process Control Workshop, Porsgrunn, Norway

24. 6. Conclusions • The Enhanced Dynamic Causal Digraph method was applied on an advanced board machine simulator and tested with four fault scenarios • The EDCDG method provides valuable additional information for fault isolation compared to the previously presented methods • The results from each four cases were very promising • In future the methods is going to be tested online on this board machine Nordic Process Control Workshop, Porsgrunn, Norway

25. Thank You for your attention! Questions? Nordic Process Control Workshop, Porsgrunn, Norway