Background

1. HYBRIDIZED DECISION APPROACH FOR FAULT DIAGNOSIS OF COMPLEX INDUSTRIAL PROCESSES- Yuba Raj AdhikariDepartment of Electrical Engineering, Pulchwok Campus,(IOE-TU)

2. Background • Industrial processes • complex processes • a lot of measurments or information available. • critical knowledge management & decision making are challenging tasks. • Fault diagnosis requires much expertise and is knowledge intensive. • Fault diagnosis includes: • Fault detection • Diagnostic decision (Fault identification and isolation)

3. Fault Detection • Model based approach • (Residual generation) • Process history based appraoch (Feature extraction) • qualitatively (EX, QTA) & • quantitatively (PCA, SOM)

4. Diagnostic Decison Fig.2 Inferencing for fault isolation

5. Objectives • To explore and analyze different fault diagnosis approaches. • To purpose an hybridized approach, Causal Based Reasoning method for isolating industrial fault.

6. Methodology • Through review of different approaches of fault diagnosis from literature. • Analysis of pros and cons of different methods. • Development of Casual based reasoning method for fault isolation. • Testing of developed method in Simulation Environment.

7. Classification of diagnostic algorithms

8. Fault Detection • Quantitative model based approach • (Residual generation) • Residual is an analytical symptom • A general assumption is that the residuals are changed significantly so that detection is possible. • The most important issue is concerned with the accuracy of the model • The uncertainty arises because of the impossibility of obtaining complete knowledge and understanding of the monitored process.

9. Fault Detection... Process History (Data) based-appraoch • A large amount of historical process data is needed • The way of generating symptoms is via information transfer from raw process data (Feature extraction) • qualitatively (EX, QTA) & • quantitatively (PCA, Statistical indices, Neural Network, SOM etc. )

10. Diagnostic Decison • Rule based reasoning • Process knowledge is collected from system experts (operators and engineers) in the form of IF-THEN rules. • Case based reasoning • CBR solves problems by adapting previoulsy • successful solutions to similar problems. • Model based reasoning • Residual evaluation • Cause-effect type of reasoning. • Statistical reasoning • Variable contribution • Prior-post probability analysis and likelyhood estimates. Fig. CBR-Cycle

11. MBR over RBR/CBR • RBR: • Rules must be pre-determined completely & correctly. • Eliciting diagnostic knowledge from experts is hard. • It may become increasingly slow with the expanding of RB. • CBR: • Unable to provide a solution to the given problem if there are no matches. • No deep explanation in problem solving • SR • basically based on data (no model is required/considerably less effort) • easy to use, • adequate to detect when the process is out-of-order, • but cannot indicate which variables are more responsible for the malfunction. • MBR (Model Based Reasoning), can handle any situations.

12. MBR • Quantitatively (first principle) getting model is complex and expensive. • Reasoning from Quantitative MBR is always a risk (model accuracy). • Qualitative MBR is more promising. In qualitative MBR based diagnosis, the system structure is described in terms of causal models: Sign directed graph (SDG), Fault tree (FT). • Fuzzy logic can include incomplete quantitative information in qualitative decision.

13. Causal model based diagnosis • A significant aspect of the knowledge required to analyze disturbed regimes is an understanding of the mechanisms in causality terms. • A casual structure is a description of the effects that variables may have on one another, and it may be represented by a directed graph (digraph). • This structure then provides a conceptual tool for reasoning about the way in which normal or abnormal changes propagate within a plant.

14. SDG-based reasoning in diagnosis • Sign diagraphs (SDG), first called by Iri, et al. (1979), are the graphs with directed arcs between the nodes. • node – a variable • each arc – Qualitative tranfer function (QTF) • In SDG, diagnosis is done for all the variables Fig. Sign directed graph (SDG)

15. Fig. An example of SDG based reasoning

16. Qualitative MBR + Fuzzy Logic • Qualitative reasoning has the ability to describe the behavior systematically. • Beside this, the qualitative reasoning suffers from incompleteness. • In process engineering, some forms of quantitative knowledge are also available (e.g. steady state gain, step change). • By means of fuzzy logic those information can be utilized to improve diagnostic resolution.

17. Fig. An Outlook of Paper Machine

18. Testing Environment (APROS) Fig. Drying machine with a fault (shown as a cross sign), a part of the paper machine in APROS

19. Fig. Residual generation for SDG based test

20. Diagnostic Decision • If the detection test is positive in local node, the fault can be the propagated fault or local fault. Check the upstring variable next to it. • if the detection test for the upstring variable is negative than the variable following the variable under detection test is faulty.

21. Fault Signature Matrix SPinis the Faulty Location, rest are propagation of this fault.

22. Conclusions • Approximating complex processes by physical laws cannot be always correct. • Incorporating knowledge (see what data says) can be an alternative approach to handle such system. • A hybridized approach is causal models. • Causal model based reasoning works well for detecting and localizing fault.