IEEE WCCI 2014
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Transcript of IEEE WCCI 2014
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Gradient-based Fault Isolation for
Residual-based Fault Detection Systems
Francisco Serdio Fernández
Department of Knowledge-Based Mathematical Systems
Johannes Kepler University Linz, Austria
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Why Fault Detection (FD) ? Why Fault Isolation (FI) ? FD with Residual-based approaches Motivation of the FI Gradient-based approaches Tools to depict Fault Isolation Results Can do we more ? Conclusions
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Why Fault Detection?
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Why Fault Detection?
Products with high quality demands High quality is required also in the production chain High quality is required also in the supply chain
High quality processes imply Continuity in the production lines Minimum down-time
[2] R. Iserman. Fault-Diagnosis Applications. Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-tolerant Systems. Springer, Berlin Heidelberg, Germany, 2011.
[1] D. Blanchard. Supply Chain Management Best Practices. John Wiley & Sons, Hoboken, NJ, USA, 2007.
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Why Fault Detection?
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Why Fault Detection?
Manual process supervision Manual supervision is not affordable or in some
cases simply impossible The precision of manual supervision usually depends
on the experience of the operators and even on their performance on a given day
[3] E. Lughofer, J.E. Smith, P. Caleb-Solly, M. Tahir, C. Eitzinger, D. Sannen and M. Nuttin. (2009). Human-machine interaction issues in quality control based on on-line image classication. IEEE Transactions on Systems, Man and Cybernetics, part A: Systems and Humans, 39(5), 960-971.
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Why Fault Detection (FD) ? Why Fault Isolation (FI) ? FD with Residual-based approaches Motivation of the FI Gradient-based approaches Tools to depict Fault Isolation Results Can do we more ? Conclusions
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Fault Isolation
Why Fault Isolation?
Haystack
Needle
Fault Detection
Needle !!
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Why Fault Isolation?
Multiple sensor networks turned out to emerge in industrial settings and factories Huge amount of sensors and actuators to check Manual supervision is not affordable or in some
cases simply impossible Any valuable information regarding where the fault is
located could be a great aid for the operator Isolation !
fault
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Why Fault Detection (FD) ? Why Fault Isolation (FI) ? FD with Residual-based approaches Motivation of the FI Gradient-based approaches Tools to depict Fault Isolation Results Can do we more ? Conclusions
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Analytical Redundancy Direct redundancy
Algebraic relationships among different sensors Temporal redundancy
Difference relationships among different sensor outputs and actuator inputs
Inconsistencies, expressed as residuals, can be used for detection and isolation purposes
[4] V. Venkatasubramanian, R. Rengaswamy, S. Kavuri and K. Yin. (2003). A review of process fault detection and diagnosis: Part iii: Process history based methods. Computers & Chemical Engineering, 27(3), 327-346.
FD with Residual-based approaches
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Analytical Redundancy graphicallyMoving from the signal space to the residual space: illustrating an untypical signal pattern
FD with Residual-based approaches
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Tracking residuals within a dynamic tolerance band
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Recall FD with Residual-based approaches
More information regarding Fault Detection in
[8] 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, vol. under revision (minor), 2014.
[6] 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, vol. 259, pp. 304–330, 2014.
[5] 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)
[7] 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.
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Why Fault Detection (FD) ? Recall FD with Residual-based approaches Why Fault Isolation (FI) ? Motivation of the FI Gradient-based approaches Tools to depict Fault Isolation Results Can do we more? Conclusions
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Motivation of the FI Gradient-based approaches
We are blind about faults We do not know how a fault looks like We do not have fault patterns (labeled data)
There is literature about PCA Process variable contribution plot
There is an extension to non-linear PCA It reverts back to the original process variables
[10] F. Jia, E. Martin, and A. Morris, Nonlinear principal components analysis with application to process fault detection. International Journal of Systems Science, vol. 31, p. 14731487, 2001.
[9] P. Miller, R. Swanson, and C. Heckler, Contribution plots: A missing link in multivariate quality control. Applied Mathematics and Computer Science, vol. 8, p. 775792, 1998.
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Motivation of the FI Gradient-based approaches
Partial derivatives ! With respect to a specific dimension can indicate the
relative importance of the corresponding variable (channel) on that function
Can be computed according to the model expression Can be computed by means of numeric tricks
We can plug a FI system to any FD model !
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
How do we revert back to the original process variables? We take the warning models
We compute the gradients of the model variables We aggregate the gradients We get a candidate responsible variable
Crisp decision
Fuzzy decision
Motivation of the FI Gradient-based approaches
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Crisp decision Winner takes all approach
Biggest gradient as faulty channel A channel is either (properly) isolated or not
Fuzzy decision Several channels are proposed as faulty There are normalized against the channel with the
highest gradient aggregation By definition, it will produce always better results than
its crisp counterpart
Aggregating gradients
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Why Fault Detection (FD) ? FD with Residual-based approaches Why Fault Isolation (FI) ? Motivation of the FI Gradient-based approaches Tools to depict Fault Isolation Results Can do we more ? Conclusions
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Tools to depict Fault Isolation
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Why Fault Detection (FD) ? FD with Residual-based approaches Why Fault Isolation (FI) ? Motivation of the FI Gradient-based approaches Tools to depict Fault Isolation Results Can do we more? Conclusions
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Results
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Results
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Why Fault Detection (FD) ? FD with Residual-based approaches Why Fault Isolation (FI) ? Motivation of the FI Gradient-based approaches Tools to depict Fault Isolation Results Can do we more ? Conclusions
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Can do we more ?
We must work in how to aggregate the gradients Weight the gradients with other data
We are using violation degree of the threshold We are using quality of the model
Time frames (sliding windows)
Goal: narrow the Fault Isolation Gap (FIG)
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Why Fault Detection (FD) ? FD with Residual-based approaches Why Fault Isolation (FI) ? Motivation of the FI Gradient-based approaches Tools to depict Fault Isolation Results Can do we more ? Conclusions
[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio
WCCI 2014 / July 6-11 / Beijing, China
Conclusions
We can perform Fault Isolation (FI) without information about the faults Only based on warning models and gradients
We have introduced new tools to depict FI Graphically Numerically
We must still strength the results
{francisco.serdio,edwin.lughofer}@jku.at http://www.flll.jku.at/staff/{francisco,lughofer}Francisco Serdio, Dr. Edwin Lughofer
WCCI 2014 / July 6-11 / Beijing, China
Thanks a lot for your attention!