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29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
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 TechnologyFaculty of Chemical Technology and Material Sciences
Research group of Process Control and Automation
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
Table of Contents
1. Introduction2. Fault Diagnosis Using Enhanced Dynamic Causal
Digraph Method3. Application of the EDCDG on an Industrial Process4. Description of the Process and the Test
Environment5. Fault Diagnosis Results6. Conclusions
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
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
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
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
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
Table of Contents
1. Introduction2. Fault Diagnosis Using Enhanced Dynamic Causal
Digraph Method3. Application of the EDCDG on an Industrial Process4. Description of the Process and the Test
Environment5. Fault Diagnosis Results6. Conclusions
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
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
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
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
Cause-effect model
Pump rotation speed
Valve opening
Mass flow rate Tank level
Cause-effect model
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
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
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
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
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
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
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
2. Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method
• The consistency between the suspected sets of arcs and process knowledge is checked
otherwise,0
offaultthe
causesarcfaulty,1
),( jU,
iU,
jiUM
otherwise,0
1,),(if,1)( aNiSiARCi
Msv
)()( Msvsv NUMNUM
suspected arcs sets
knowledge matrix
inference
decreased number of suspected arcs sets
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
Table of Contents
1. Introduction2. Fault Diagnosis Using Enhanced Dynamic Causal
Digraph Method3. Application of the EDCDG on an Industrial Process4. Description of the Process and the Test
Environment5. Fault Diagnosis Results6. Conclusions
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
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
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
Table of Contents
1. Introduction2. Fault Diagnosis Using Enhanced Dynamic Causal
Digraph Method3. Application of the EDCDG on an Industrial Process4. Description of the Process and the Test
Environment5. Fault Diagnosis Results6. Conclusions
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
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
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
Table of Contents
1. Introduction2. Fault Diagnosis Using Enhanced Dynamic Causal
Digraph Method3. Application of the EDCDG on an Industrial Process4. Description of the Process and the Test
Environment5. Fault Diagnosis Results6. Conclusions
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
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 modeltypes were used
• First principles models• Static regression• Neural networks
– Also the knowledge matrixM was obtained
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
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
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
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
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
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
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
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
4 3 22 1 2 1 2 1 315
( ) ( )NUM NUM sv sv M
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
5. Fault Diagnosis Results
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
Table of Contents
1. Introduction2. Fault Diagnosis Using Enhanced Dynamic Causal
Digraph Method3. Application of the EDCDG on an Industrial Process4. Description of the Process and the Test
Environment5. Fault Diagnosis Results6. Conclusions
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
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
29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway
Thank You for your attention!
Questions?