TY - GEN
T1 - Time-Series-Based Clustering for Failure Analysis in Hardware-in-the-Loop Setups
T2 - 31st IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2020
AU - Jordan, Claudius V.
AU - Hauer, Florian
AU - Foth, Philipp
AU - Pretschner, Alexander
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - Testing is an important cost driver in development projects. Especially in the automotive industry, immense efforts are spent to carry out validation facing increasingly complex systems. Hardware-in-the-Loop test benches are essential elements for (functional) validation. Naturally, failures commonly occur, whose analysis is challenging, time-consuming and oftentimes performed manually, making the diagnosis process one decisive cost-driving factor. By experience, many failures happen due to few underlying faults. We discuss our lessons learned when performing similarity-based clustering to identify representative tests for each fault for system-level testing where test execution times are high and the complexity of the system-under-test and also the test setup leads to complicated failure conditions. Results from an industrial automotive case study-a drive train system dataset consisting of 57 test runs-show that utilizing our general, project-agnostic approach can effectively reduce failure analysis time even with a limited set of data points.
AB - Testing is an important cost driver in development projects. Especially in the automotive industry, immense efforts are spent to carry out validation facing increasingly complex systems. Hardware-in-the-Loop test benches are essential elements for (functional) validation. Naturally, failures commonly occur, whose analysis is challenging, time-consuming and oftentimes performed manually, making the diagnosis process one decisive cost-driving factor. By experience, many failures happen due to few underlying faults. We discuss our lessons learned when performing similarity-based clustering to identify representative tests for each fault for system-level testing where test execution times are high and the complexity of the system-under-test and also the test setup leads to complicated failure conditions. Results from an industrial automotive case study-a drive train system dataset consisting of 57 test runs-show that utilizing our general, project-agnostic approach can effectively reduce failure analysis time even with a limited set of data points.
KW - automotive
KW - clustering
KW - failure analysis
KW - multi-dimensional time-series
UR - http://www.scopus.com/inward/record.url?scp=85099811793&partnerID=8YFLogxK
U2 - 10.1109/ISSREW51248.2020.00039
DO - 10.1109/ISSREW51248.2020.00039
M3 - Conference contribution
AN - SCOPUS:85099811793
T3 - Proceedings - 2020 IEEE 31st International Symposium on Software Reliability Engineering Workshops, ISSREW 2020
SP - 67
EP - 72
BT - Proceedings - 2020 IEEE 31st International Symposium on Software Reliability Engineering Workshops, ISSREW 2020
A2 - Vieira, Marco
A2 - Madeira, Henrique
A2 - Antunes, Nuno
A2 - Zheng, Zheng
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 October 2020 through 15 October 2020
ER -