An Anomaly Detection Approach to Detect Unexpected Faults in Recordings from Test Drives
In the automotive industry test drives are being conducted
during the development of new vehicle models or as a part of
quality assurance of series-production vehicles. The communication
on the in-vehicle network, data from external sensors, or internal
data from the electronic control units is recorded by automotive
data loggers during the test drives. The recordings are used for fault
analysis. Since the resulting data volume is tremendous, manually
analysing each recording in great detail is not feasible.
This paper proposes to use machine learning to support domainexperts
by preventing them from contemplating irrelevant data and
rather pointing them to the relevant parts in the recordings. The
underlying idea is to learn the normal behaviour from available
recordings, i.e. a training set, and then to autonomously detect
unexpected deviations and report them as anomalies.
The one-class support vector machine “support vector data description”
is utilised to calculate distances of feature vectors. SVDDSUBSEQ
is proposed as a novel approach, allowing to classify subsequences
in multivariate time series data. The approach allows to
detect unexpected faults without modelling effort as is shown with
experimental results on recordings from test drives.
<p>[1] K. Lamberg, “Model-based testing of automotive electronics,” Design,
Automation and Test in Europe Conference and Exhibition, vol. 1, p. 28,
2006.
[2] M. Kr¨amer, A. R¨ohringer, W. Kirchner, T. Vogel, and J. Rochlitzer,
“Programme Management and Project Control,” ATZ extra. The New
E-Class by Mercedes-Benz, 2009.
[3] C. Marscholik and P. Subke, Road vehicles – Diagnostic communication.
H¨uthig GmbH und Co. KG, 2008.
[4] “ISO 26262: Road vehicles – Functional safety – Part 1: Vocabulary.
Final Draft.” 2011.
[5] I. Sommerville, Software Engineering (6th Edition). Redwood City,
CA, USA: Addison Wesley Longman Publishing Co., Inc., 2001.
[6] R. Isermann, Fault-Diagnosis Systems – An Introduction from Fault
Detection to Fault Tolerance, 1st ed. Springer, 2006.
[7] B. Stein, O. Niggemann, and H. Balzer, “Diagnosis in Automotive
Applications: A Case Study with the Model Compilation Approach,”
in Third Monet Workshop on Model-Based Systems at the ECAI, 2006,
pp. 34–40.
[8] V. Venkatasubramanian, R. Rengaswamy, K. Yin, and S. N. Kavuri,
“A review of process fault detection and diagnosis: Part I: Quantitative
model-based methods,” Computers and Chemical Engineering, vol. 27,
no. 3, pp. 293–311, 2003.
[9] V. Chandola, “Anomaly detection for symbolic sequences and time series
data,” Ph.D. dissertation, Computer Science Department, University of
Minnesota, 2009.
[10] V. J. Hodge and J. Austin, “A survey of outlier detection methodologies,”
Artificial Intelligence Review, vol. 22, p. 2004, 2004.
[11] S. Theodoridis and K. Koutroumbas, Pattern Recognition, Fourth Edition,
4th ed. Academic Press, 2009.
REFERENCES
[12] S. Abe, Support Vector Machines for Pattern Classification (Advances
in Pattern Recognition), 2nd ed. Springer-Verlag London Ltd., 2010.
[13] D. M. Tax and R. P. Duin, “Data domain description using support
vectors,” in Proceedings of the European Symposium on Artificial Neural
Networks, 1999, pp. 251–256.
[14] A. Theissler and I. Dear, “Detecting anomalies in recordings from test
drives based on a training set of normal instances,” in Proceedings of the
IADIS International Conference Intelligent Systems and Agents 2012 and
European Conference Data Mining 2012. IADIS Press, Lisbon., 2012,
pp. 124–132.
[15] D. M. Tax, “One-class classification. concept-learning in the absence of
counter-examples,” Ph.D. dissertation, Delft University of Technology,
2001.
[16] C. A. Jones, “Lecture notes: Math2640 introduction to optimisation 4,”
University of Leeds, School of Mathematics, Tech. Rep., 2005.
[17] D. Tax and R. Duin, “Support vector data description,” Machine Learning,
vol. 54, no. 1, pp. 45–66, Jan. 2004.
[18] A. Theissler and I. Dear, “Autonomously determining the parameters for
SVDD with RBF kernel from a one-class training set,” in Proceedings
of the WASET International Conference on Machine Intelligence 2013,
Stockholm (to be published)., 2013.
[19] T. Mitsa, Temporal Data Mining. Chapman & Hall/CRC, 2010.
[20] T. Raykov and G. Marcoulides, Basic Statistics: An Introduction with
R. Rowman & Littlefield Publishers, 2012.
[21] J. Laurikkala, M. Juhola, and E. Kentala, “Informal identification of
outliers in medical data,” in 14th European Conference on Artificial
Intelligence ECAI-2000. Berlin, 2000.
[22] R. Etzold, So wird’s gemacht. Pflegen - warten - reparieren: Renault
Twingo von 6/93 bis 12/06, 8th ed. Delius Klasing, 2011.
[23] A. Theissler, D. Ulmer, and I. Dear, “Interactive knowledge discovery
in recordings from vehicle tests,” in 33rd FISITA World Automotive
Congress. FISITA, 2010.</p>
<p>[1] K. Lamberg, “Model-based testing of automotive electronics,” Design,
Automation and Test in Europe Conference and Exhibition, vol. 1, p. 28,
2006.
[2] M. Kr¨amer, A. R¨ohringer, W. Kirchner, T. Vogel, and J. Rochlitzer,
“Programme Management and Project Control,” ATZ extra. The New
E-Class by Mercedes-Benz, 2009.
[3] C. Marscholik and P. Subke, Road vehicles – Diagnostic communication.
H¨uthig GmbH und Co. KG, 2008.
[4] “ISO 26262: Road vehicles – Functional safety – Part 1: Vocabulary.
Final Draft.” 2011.
[5] I. Sommerville, Software Engineering (6th Edition). Redwood City,
CA, USA: Addison Wesley Longman Publishing Co., Inc., 2001.
[6] R. Isermann, Fault-Diagnosis Systems – An Introduction from Fault
Detection to Fault Tolerance, 1st ed. Springer, 2006.
[7] B. Stein, O. Niggemann, and H. Balzer, “Diagnosis in Automotive
Applications: A Case Study with the Model Compilation Approach,”
in Third Monet Workshop on Model-Based Systems at the ECAI, 2006,
pp. 34–40.
[8] V. Venkatasubramanian, R. Rengaswamy, K. Yin, and S. N. Kavuri,
“A review of process fault detection and diagnosis: Part I: Quantitative
model-based methods,” Computers and Chemical Engineering, vol. 27,
no. 3, pp. 293–311, 2003.
[9] V. Chandola, “Anomaly detection for symbolic sequences and time series
data,” Ph.D. dissertation, Computer Science Department, University of
Minnesota, 2009.
[10] V. J. Hodge and J. Austin, “A survey of outlier detection methodologies,”
Artificial Intelligence Review, vol. 22, p. 2004, 2004.
[11] S. Theodoridis and K. Koutroumbas, Pattern Recognition, Fourth Edition,
4th ed. Academic Press, 2009.
REFERENCES
[12] S. Abe, Support Vector Machines for Pattern Classification (Advances
in Pattern Recognition), 2nd ed. Springer-Verlag London Ltd., 2010.
[13] D. M. Tax and R. P. Duin, “Data domain description using support
vectors,” in Proceedings of the European Symposium on Artificial Neural
Networks, 1999, pp. 251–256.
[14] A. Theissler and I. Dear, “Detecting anomalies in recordings from test
drives based on a training set of normal instances,” in Proceedings of the
IADIS International Conference Intelligent Systems and Agents 2012 and
European Conference Data Mining 2012. IADIS Press, Lisbon., 2012,
pp. 124–132.
[15] D. M. Tax, “One-class classification. concept-learning in the absence of
counter-examples,” Ph.D. dissertation, Delft University of Technology,
2001.
[16] C. A. Jones, “Lecture notes: Math2640 introduction to optimisation 4,”
University of Leeds, School of Mathematics, Tech. Rep., 2005.
[17] D. Tax and R. Duin, “Support vector data description,” Machine Learning,
vol. 54, no. 1, pp. 45–66, Jan. 2004.
[18] A. Theissler and I. Dear, “Autonomously determining the parameters for
SVDD with RBF kernel from a one-class training set,” in Proceedings
of the WASET International Conference on Machine Intelligence 2013,
Stockholm (to be published)., 2013.
[19] T. Mitsa, Temporal Data Mining. Chapman & Hall/CRC, 2010.
[20] T. Raykov and G. Marcoulides, Basic Statistics: An Introduction with
R. Rowman & Littlefield Publishers, 2012.
[21] J. Laurikkala, M. Juhola, and E. Kentala, “Informal identification of
outliers in medical data,” in 14th European Conference on Artificial
Intelligence ECAI-2000. Berlin, 2000.
[22] R. Etzold, So wird’s gemacht. Pflegen - warten - reparieren: Renault
Twingo von 6/93 bis 12/06, 8th ed. Delius Klasing, 2011.
[23] A. Theissler, D. Ulmer, and I. Dear, “Interactive knowledge discovery
in recordings from vehicle tests,” in 33rd FISITA World Automotive
Congress. FISITA, 2010.</p>
@article{"International Journal of Information, Control and Computer Sciences:56165", author = "Andreas Theissler and Ian Dear", title = "An Anomaly Detection Approach to Detect Unexpected Faults in Recordings from Test Drives", abstract = "In the automotive industry test drives are being conducted
during the development of new vehicle models or as a part of
quality assurance of series-production vehicles. The communication
on the in-vehicle network, data from external sensors, or internal
data from the electronic control units is recorded by automotive
data loggers during the test drives. The recordings are used for fault
analysis. Since the resulting data volume is tremendous, manually
analysing each recording in great detail is not feasible.
This paper proposes to use machine learning to support domainexperts
by preventing them from contemplating irrelevant data and
rather pointing them to the relevant parts in the recordings. The
underlying idea is to learn the normal behaviour from available
recordings, i.e. a training set, and then to autonomously detect
unexpected deviations and report them as anomalies.
The one-class support vector machine “support vector data description”
is utilised to calculate distances of feature vectors. SVDDSUBSEQ
is proposed as a novel approach, allowing to classify subsequences
in multivariate time series data. The approach allows to
detect unexpected faults without modelling effort as is shown with
experimental results on recordings from test drives.
", keywords = "Anomaly detection, fault detection, test drive analysis,
machine learning.", volume = "7", number = "7", pages = "937-8", }
