1 / 27

Fault Detection and Diagnosis (II)

Fault Detection and Diagnosis (II). Qualitative Model-based Methods. Qualitative Model-based. Causal Models. Abstraction Hierarchy. Diagraphs. Qualitative Physics. Fault trees. Structural. Functional. Input. Process. Output. Diagraph.

doris
Download Presentation

Fault Detection and Diagnosis (II)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Fault Detection and Diagnosis(II)

  2. Qualitative Model-based Methods Qualitative Model-based Causal Models Abstraction Hierarchy Diagraphs Qualitative Physics Fault trees Structural Functional

  3. Input Process Output Diagraph • Signed diagraph (SDG): representing cause-effect • Nodes: • Input: • Process: • Output:

  4. Tank Example F1 Z F2

  5. Model • SDG F1 dZ F2 Z

  6. Fault Tree • Definition: a logical tree that propagates primary events (faults) to top level event or fault • Fault tree analysis • System definition • Fault tree construction • Qualitative evaluation • Quantitative evaluation

  7. Fault Tree • Work from top level event down to primary event • Find the cause (a lower level event) of each event • Use OR, AND and XOR

  8. Fault Tree Example C D B A

  9. Qualitative Physics • Common sense • Qualitative (confluence) eqs from ODEs • Causal ordering • Precedence ordering: information flow • Qualitative behavior from ODEs • Qualitative simulation (QSIM) • Dynamic, require constraints and IC • Qualitative process theory (QPT) • Processobjectsstates parameters (quantity)

  10. Abstraction Hierarchy • Decompose the system into units • Construct the input-output relationship • Structural: connectivity • Functional: outputs=f(inputs)

  11. Search Methods • Topographic: • use template of normal operation • No assumption for fault required • Symptomatic: • look for symptoms for direction • Information economy

  12. Topographic Search • Structural decomposition • Identify the information flow path,include all the subcomponents within the path of fault • Select subpaths to localize the fault • Functional decomposition • Search in terms of functionality

  13. Symptomatic Search • Require defined symptoms, difficult to detect multiple faults • Require complete knowledge of symptoms, difficult to detect novel faults • Approaches • Look-up table: fault template • Hypothesis and test search: on-line generated hypothesis

  14. Process History Based Methods • Qualitative • Expert systems • Trend modeling • Quantitative • Statistical • PCA,PLS, Statistical pattern classifiers • Non-statistical • Neural network

  15. Process History-Based Qualitative Quantitative QTA Statistical Non-Statistical Expert Systems PCA/PLS Statistical classifiers Neural Network

  16. Expert System • Rule-based feature extraction • Components • Knowledge acquisition • Choice of knowledge representation • Coding • Development of inference procedures • Development of input-output interface

  17. Expert System • Advantages • Easy to develop • Transparent reasoning • Able to reason under uncertainty • Able to provide explanation • Disadvantages • System-specific • Difficult to update

  18. Qualitative Trend Analysis • Predict future events • Filters needed • Detect a fault from a distinct trend • May confuse transit with fault

  19. Quantitative Feature Extraction • Statistical • Measurements are considered statistical time series • Fault changes the underlying distribution (μ and σ) • Stop region • Test statistic • Statistic process control chart: Shewhart

  20. Quantitative Feature Extraction • Multivariate Statistical • Principle Components Analysis (PCA) • Transform related variables to lower dimension uncorrelated variables • Partial Least Squares (PLS) • Reduce process/product quality variables

  21. PCA • X and covariance

  22. PLS • PLS model – X: Predictor matrix (process variables) • Y: Predicted matrix (product quality) • Find regression X->Y that maximizes covariance between X and Y • First few latent variables explain the covariance

  23. Y h<δ? N C1 C2 Statistical Classifier

  24. Comparison of Approaches • Quantitative model-based • Advantages • Fault diagnosis is well defined if complete knowledge is available • Provide design schemes to minimize disturbance effect • Disadvantages • Limited to linear and few nonlinear models • Modeling cost may be prohibitive

  25. Comparison of Approaches • Qualitative model-based approaches • Advantage • When quantitative models are not available and fundamentals are understood • Provide explanation of path of the propagation of fault • Disadvantage • Resolution from ambiguity

  26. Comparison of Approaches • Process history based methods • Advantages • No model needed • Robust to noise • Meet isolability requirement • Disadvantage • Performance limited by training data

More Related