IEEE WCCI 2014

Gradient-based Fault Isolation
Residual-based Fault Detection Systems
Francisco Serdio Fernández
Department of Knowledge-Based Mathematical Systems
Johannes Kepler University Linz, Austria
WCCI 2014 / July 6-11 / Beijing, China
francisco.serdio@jku.at
for
http://www.flll.Francisco Serdio jku.at/staff/francisco

 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
[1] D. Blanchard. Supply Chain Management Best Practices. John Wiley & Sons,
Hoboken, NJ, USA, 2007.
 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.
 High quality processes imply

 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.
 Manual process supervision

Fault Isolation
Detection
Needle
Needle !!
Why Fault Isolation?
Haystack

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 !

FD with Residual-based approaches
 Algebraic relationships among different sensors
 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.
 Analytical Redundancy
 Direct redundancy
 Temporal redundancy

FD with Residual-based approaches
Analytical Redundancy graphically
Moving from the signal space to the residual space: illustrating an untypical signal pattern

Tracking residuals within a dynamic tolerance band

Recall FD with Residual-based
approaches
 More information regarding Fault Detection in
[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)
[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.
[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.
[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.

 Recall FD with Residual-based approaches
 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)
 Process variable contribution plot
 There is an extension to non-linear PCA
 It reverts back to the original process variables
[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.
[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.
 There is literature about PCA

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 !

approaches
 How do we revert back to the original process
variables?
 We compute the gradients of the model variables
 We aggregate the gradients
 We get a candidate responsible variable
 We take the warning models
 Crisp decision
 Fuzzy decision

Aggregating gradients
 Biggest gradient as faulty channel
 A channel is either (properly) isolated or not
 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
 Crisp decision
 Winner takes all approach
 Fuzzy decision

 Results
 Can do we more ?
 Conclusions

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
 Goal: narrow the Fault Isolation Gap (FIG)
 Time frames (sliding windows)

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
 We must still strength the results
 Graphically
 Numerically

Thanks a lot for your attention!
{francisco.serdio,edwin.lughofer}@jku.at
http://www.flll.jku.at/staff/{Francisco Serdio, Dr. Edwin Lughofer francisco,lughofer}

IEEE WCCI 2014

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