NABIC 2014

[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio

NABIC 2014 – Porto, July 30,31 - August 1, 2014

Hybrid Genetic-Fuzzy Systems for Improved Performance

in Residual-Based Fault Detection

Francisco Serdio Fernández

Department of Knowledge-Based Mathematical Systems

Johannes Kepler University Linz, Austria



IntroductionApproachResultsConclusions



Fault Detection

Operator Monitoring Tools

System



System models Allow to detect faults of small sizes Difficult or impossible for many systems

Limited for systems with simple equations

Expert systems Represent the expert knowledge

Fault Patterns Allow Pattern Recognition and Classification

approaches we know how a fault looks like

Expert Knowledge

J. Korbicz, J.M. Koscielny, Z. Kowalczuk and W. Cholewa (Eds.). Fault Diagnosis: Models, Artificial Intelligence, Applications. Springer-Verlag. Berlin Heidelberg. 2004.



J. Korbicz, J.M. Koscielny, Z. Kowalczuk and W. Cholewa (Eds.). Fault Diagnosis: Models, Artificial Intelligence, Applications. Springer-Verlag. Berlin Heidelberg. 2004.

Expert Knowledge



Identify systems (Sys Id) Discover dependencies between variables

Build models Represent the ground truth of the systems Starting point to produce residual signals

Compute residuals We move to the residual space

Manage residuals We can decide whether there is or not a fault

FD without Expert Knowledge



Running Fault Detection System



Analytical Redundancy graphicallyMoving from the signal space to the regression line we can graphically illustrate an untypical signal pattern

FD with Residual-based approaches



FD with Residual-based approaches More information regarding Fault Detection in

F. Serdio, E. Lughofer, K. Pichler, T. Buchegger and H. Efendic, Fault Detection in Multisensor Networks based on Multivariate Time-series Models and Orthogonal Transformations. Information Fusion, (to appear), 2014.

F. Serdio, E. Lughofer, K. Pichler, T. Buchegger and H. Efendic, Residual-based Fault Detection using Soft Computing techniques for Condition Monitoring at Rolling Mills. Information Sciences, 259, pp. 304–330, 2014.

F. Serdio, E. Lughofer, K. Pichler, T. Buchegger and H. Efendic, Data-Driven Residual-Based Fault Detection for Condition Monitoring in Rolling Mills. Proceedings of the IFAC Conference on Manufacturing Modeling, Management and Control, MIM '2013, St. Petersburg, Russia, 2013, pp. 1546-1551. (Winner of MIM 2013 Best paper award)

F. Serdio, E. Lughofer, K. Pichler, T. Buchegger, M. Pichler and H. Efendic, Multivariate Fault Detection using Vector Autoregressive Moving Average and Orthogonal Transformation in the residual Space. Annual Conference of the Prognostics and Health Management Society, PHM 2013, New Orleans, LA, USA, 2013, pp. 548-555.



Problems - Low Quality Models

Less residual generators Fault detection performance decreases



Build more residual generators Would increase the Fault Detection Performance

How? Using Genetic Fuzzy Systems

Where? In channels without a good quality model

Our Approach



Our Approach



Genetic Fuzzy Systems

Codification of an Individual Represent an individual Fuzzy System




Initial population “Smart” individuals Random individuals




Crossover (rate 80%) We have extended the Random Convex Crossover

Dumitru Dumitrescu, Beatrice Lazzerini, Lakhmi C Jain, and Anca Dumitrescu. Evolutionary computation, volume 18. CRC press, 2000.

Mutation (rate 15%) We used Single Point Mutation

Selection We used Random Selection

Replacement We used Elitism



Crossover

Applied to μ, σ, β, ω separately Avoid to be disruptive

Behavior Parents A, B Offspring X, Y Example with centers μ

1. Select random in [-0.2, 0.5]

2. Select random rules to cross

3. Create the new centers by



Fitness

Training and Test 80% for training, 20% for validation

Trains the Fuzzy Systems of the individual Asses the quality of the Fuzzy System

Mean Squared Error (MSE) Uses training set The last generation uses the validation set

Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2nd edition, 2009.



Testing Environment

We tested a real scenario engine test bench We used artificial faults

100 faults 50 runs * 2 faults / run 5 fault intensities 5%, 10%, 20%, 50%, 100%



Introducing faults in the data



Results



Conclusions

More residuals generators Can be build by Genetic Fuzzy Systems Improve the Fault Detection performance

There is room for improvement Add operators to

Merge rules Add / remove rules



Thanks a lot for your attention!

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