Time-Series-Based Clustering for Failure Analysis in Hardware-in-the-Loop Setups: An Automotive Case Study
Claudius V. Jordan, Florian Hauer, Philipp Foth, Alexander Pretschner · 2020
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.