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



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



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.




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.



Fault Isolation

Why Fault Isolation?

Haystack

Needle

Fault Detection

Needle !!



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



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



Analytical Redundancy graphicallyMoving from the signal space to the residual space: illustrating an untypical signal pattern

FD with Residual-based approaches



Tracking residuals within a dynamic tolerance band



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.



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



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.




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 !



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




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



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



Tools to depict Fault Isolation



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



Results



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)



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


Thanks a lot for your attention!

ieee wcci 2014

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