TY - JOUR
T1 - IRVINE: A Design Study on Analyzing Correlation Patterns of Electrical Engines
AU - Eirich, Joscha
AU - Bonart, Jakob
AU - Jackle, Dominik
AU - Sedlmair, Michael
AU - Schmid, Ute
AU - Fischbach, Kai
AU - Schreck, Tobias
AU - Bernard, Jürgen
N1 - Publisher Copyright:
IEEE
PY - 2022/1/1
Y1 - 2022/1/1
N2 - In this design study, we present IRVINE, a Visual Analytics (VA) system, which facilitates the analysis of acoustic data to detect and understand previously unknown errors in the manufacturing of electrical engines. In serial manufacturing processes, signatures from acoustic data provide valuable information on how the relationship between multiple produced engines serves to detect and understand previously unknown errors. To analyze such signatures, IRVINE leverages interactive clustering and data labeling techniques, allowing users to analyze clusters of engines with similar signatures, drill down to groups of engines, and select an engine of interest. Furthermore, IRVINE allows to assign labels to engines and clusters and annotate the cause of an error in the acoustic raw measurement of an engine. Since labels and annotations represent valuable knowledge, they are conserved in a knowledge database to be available for other stakeholders. We contribute a design study, where we developed IRVINE in four main iterations with engineers from a company in the automotive sector. To validate IRVINE, we conducted a field study with six domain experts. Our results suggest a high usability and usefulness of IRVINE as part of the improvement of a real-world manufacturing process. Specifically, with IRVINE domain experts were able to label and annotate produced electrical engines more than 30% faster.
AB - In this design study, we present IRVINE, a Visual Analytics (VA) system, which facilitates the analysis of acoustic data to detect and understand previously unknown errors in the manufacturing of electrical engines. In serial manufacturing processes, signatures from acoustic data provide valuable information on how the relationship between multiple produced engines serves to detect and understand previously unknown errors. To analyze such signatures, IRVINE leverages interactive clustering and data labeling techniques, allowing users to analyze clusters of engines with similar signatures, drill down to groups of engines, and select an engine of interest. Furthermore, IRVINE allows to assign labels to engines and clusters and annotate the cause of an error in the acoustic raw measurement of an engine. Since labels and annotations represent valuable knowledge, they are conserved in a knowledge database to be available for other stakeholders. We contribute a design study, where we developed IRVINE in four main iterations with engineers from a company in the automotive sector. To validate IRVINE, we conducted a field study with six domain experts. Our results suggest a high usability and usefulness of IRVINE as part of the improvement of a real-world manufacturing process. Specifically, with IRVINE domain experts were able to label and annotate produced electrical engines more than 30% faster.
KW - engines
KW - acoustics
KW - labeling
KW - annotations
KW - task analysis
KW - automotive engineering
KW - data visualization
KW - Design study
KW - interactive clustering
KW - Acoustics
KW - Task analysis
KW - Engines
KW - Automotive engineering
KW - Annotations
KW - interactive labeling
KW - Data visualization
KW - Labeling
KW - User Interfaces-Graphical user interfaces (GUI)
KW - User-centered design
KW - H.5.2 [Information Interfaces and Presentation]
KW - Engines, Acoustics, Labeling, Annotations, Task analysis, Automotive engineering , Data visualization, Design study , interactive labeling , interactive clustering
UR - http://www.scopus.com/inward/record.url?scp=85118642692&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2021.3114797
DO - 10.1109/TVCG.2021.3114797
M3 - Article
SN - 1077-2626
VL - 28
SP - 11
EP - 21
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 1
ER -