research-article

IRVINE: A Design Study on Analyzing Correlation Patterns of Electrical Engines

Authors: Joscha Eirich, Jakob Bonart, Dominik Jäckle, Michael Sedlmair, Ute Schmid, Kai Fischbach, Tobias Schreck, Jürgen BernardAuthors Info & Claims

IEEE Transactions on Visualization and Computer Graphics, Volume 28, Issue 1

Pages 11 - 21

https://doi.org/10.1109/TVCG.2021.3114797

Published: 01 January 2022 Publication History

Abstract

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.

References

[1]

W. Aigner, S. Miksch, H. Schumann, and C. Tominski. Visualization of Time-Oriented Data. Springer, 1st ed., 2011.

[2]

S. Amershi, M. Cakmak, W. B. Knox, and T. Kulesza. Power to the people: The role of humans in interactive machine learning. in AI Magazine, 35 (4): pp. 105–120, 2014.

[3]

G. Andrienko, N. Andrienko, S. Rinzivillo, M. Nanni, D. Pedreschi, and F. Giannotti. Interactive visual clustering of large collections of trajectories. In Visual Analytics Science and Technology, pp. 3–10, 2009.

[4]

B. Bach, N. Henry-Riche, T. Dwyer, T. Madhyastha, J.-D. Fekete, and T. Grabowski. Small multipiles: Piling time to explore temporal patterns in dynamic networks. Computer Graphics Forum, 34, 2015.

[5]

A. Bangor, P. Kortum, and J. Miller. Determining what individual sus scores mean: Adding an adjective rating scale. Journal of Usability Studies, 4: pp. 114–123, 2009.

Digital Library

[6]

M. Behrisch, F. Korkmaz, L. Shao, and T. Schreck. Feedback-driven interactive exploration of large multidimensional data supported by visual classifier. In Visual Analytics Science and Technology, pp. 43–52, 2014.

[7]

S. Berg, D. Kutra, T. Kroeger, C. Straehle, B. Kausler, C. Haubold, M. Schiegg, J. Ales, T. Beier, M. Rudy, K. Eren, J. Cervantes, B. Xu, F. Beuttenmueller, A. Wolny, C. Zhang, U. Köthe, F. Hamprecht, and A. Kreshuk. ilastik: interactive machine learning for (bio)image analysis. Nature Methods, 16: pp. 1–7, 2019.

[8]

J. Bernard, D. Daberkow, D. Fellner, K. Fischer, O. Koepler, J. Kohlhammer, M. Runnwerth, T. Ruppert, T. Schreck, and I. Sens. Visinfo: a digital library system for time series research data based on exploratory search—a user-centered design approach. International Journal on Digital Libraries, 16 (1), 2015.

[9]

J. Bernard, M. Hutter, M. Zeppelzauer, D. Fellner, and M. Sedlmair. Comparing visual-interactive labeling with active learning: An experimental study. Transactions on Visualization and Computer Graphics, PP: 1–1, 2017.

[10]

J. Bernard, C. Ritter, D. Sessler, M. Zeppelzauer, J. Kohlhammer, and D. Fellner. Visual-interactive similarity search for complex objects by example of soccer player analysis. In Computer Vision, Imaging and Computer Graphics Theory and Applications, vol. 3, pp. 75–87, 2017.

[11]

J. Bernard, D. Sessler, J. Kohlhammer, and R. A. Ruddle. Using dashboard networks to visualize multiple patient histories: A design study on postoperative prostate cancer. Transactions on Visualization and Computer Graphics, 25 (3): pp. 1615–1628, 2019.

[12]

J. Bernard, N. Wilhelm, B. Krüger, T. May, T. Schreck, and J. Kohlhammer. Motionexplorer: Exploratory search in human motion capture data based on hierarchical aggregation. Transactions on Visualization and Computer Graphics, 19 (12): pp. 2257–2266, 2013.

Digital Library

[13]

J. Bernard, M. Zeppelzauer, M. Sedlmair, and W. Aigner. Vial: a unified process for visual interactive labeling. The Visual Computer, 34, 2018.

[14]

M. Bostock, V. Ogievetsky, and J. Heer. D-3: Data-driven documents. transactions on visualization and computer graphics, 17: 2301–9, 2011.

[15]

L. J. Brattain, B. A. Telfer, M. Dhyani, J. R. Grajo, and A. E. Samir. Machine learning for medical ultrasound: status, methods, and future opportunities. Abdominal Radiology, 43 (4): pp. 786–799, 2018.

[16]

E. T. Brown, J. Liu, C. E. Brodley, and R. Chang. Dis-function: Learning distance functions interactively. In Visual Analytics Science and Technology, pp. 83–92, 2012.

[17]

V. W. Chao, Y. Wu, L. Zhao, V. Tsai, and C.-H. Chen. Seismologically determined bedload flux during the typhoon season. Scientific Reports, 5, 2015.

[18]

K. Charmaz. Constructing grounded theory: a practical guide through qualitative analysis. London; Thousand Oaks, Calif.: Sage Publications, 2006.

[19]

L. Cibulski, H. Mitterhofer, T. May, and J. Kohlhammer. Paved: Pareto front visualization for engineering design. Computer Graphics Forum, 39: pp. 405–416, 2020.

[20]

J. J. Dudley and P. O. Kristensson. A review of user interface design for interactive machine learning. Transactions on Interactive Intelligent Systems, 8 (2), 2018.

[21]

J. Eirich, D. Jäckle, T. Schreck, J. Bonart, O. Posegga, and K. Fischbach. Vima: Modeling and visualization of high dimensional machine sensor data leveraging multiple sources of domain knowledge. In Visualization in Data Science at IEEE VIS, 2020.

[22]

J. Eirich, D. Jäckle, S. Werrlich, and T. Schreck. Visual analytics in organizational knowledge creation: A case study. In European Conference. on Information Systems, 042021.

[23]

J. L. Flanagan. Speech analysis; synthesis and perception. Springer, 2nd ed., 1972.

[24]

M. Hardin, D. Hom, and L. Williams. Which chart or graph is right for you?, 2016.

[25]

F. Heimerl, S. Koch, H. Bosch, and T. Ertl. Visual classifier training for text document retrieval. Transactions on Visualization and Computer Graphics, 18 (12): pp. 2839–2848, 2012.

Digital Library

[26]

S. Hughes. Medical ultrasound imaging. Physics Education, 36: pp. 468, 2001.

[27]

B. Höferlin, R. Netzel, M. Höferlin, D. Weiskopf, and G. Heidemann. Inter-active learning of ad-hoc classifiers for video visual analytics. In Visual Analytics Science and Technology, pp. 23–32, 2012.

[28]

A. Kampker, K. Kreiskother, N. Lutz, V. Gauckler, and M. Hehl. Re-ramp-up management of scalable production systems in the automotive industry. In Industrial Technology and Management, pp. 137–141, 2019.

[29]

D. A. Keim. Information visualization and visual data mining. Transactions on Visualization and Computer Graphics, 8 (1): pp. 1–8, 2002.

Digital Library

[30]

S. Kim, B. K. Seo, J. Lee, K. Cho, K. Lee, B.-K. Je, H. Y. Kim, Y.-S. Kim, and J.-H. Lee. Correlation of ultrasound findings with histology, tumor grade, and biological markers in breast cancer. Acta oncologica, 47: pp. 1531–8, 2008.

[31]

T. Kohonen. Essentials of the self-organizing map. Neural Networks, 37: pp. 52–65, 2013.

Digital Library

[32]

T. Kohonen, M. R. Schroeder, and T. S. Huang. Self-Organizing Maps. Berlin, Heidelberg: Springer, 3rd ed., 2001.

Digital Library

[33]

H. Lam, E. Bertini, P. Isenberg, C. Plaisant, and S. Carpendale. Empirical studies in information visualization: Seven scenarios. Transactions on visualization and computer graphics, 18, 2011.

[34]

J. Moehrmann, S. Bernstein, T. Schlegel, G. Werner, and G. Heidemann. “Improving the usability of hierarchical representations for interactively labeling large image data sets”. In Human computer interactions, pp. 618–627. Berlin, Heidelberg: Springer, 2011.

[35]

T. Munzner. A nested model for visualization design and validation. Visualization and Computer Graphics, 15: pp. 921 – 928, 2010.

[36]

I. Nonaka and H. Takeuchi. The knowledge-creating company: How japanese companies create the dynamics of innovation. New York: Oxford University Press, 1995.

[37]

A. Pister, P. Buono, J.-D. Fekete, C. Plaisant, and P. Valdivia. Integrating prior knowledge in mixed initiative social network clustering, 2020.

[38]

C. Ratanamahatana, J. Lin, D. Gunopulos, E. Keogh, M. Vlachos, and G. Das. Mining Time Series Data, pp. 1049–1077. Springer, 2010.

[39]

C. Rudin. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1: pp. 206–215, 2019.

[40]

T. Ruppert, M. Staab, A. Bannach, H. Lucke-Tieke, J. Bernard, A. Kuijper, and J. Kohlhammer. Visual interactive creation and validation of text clustering workflows to explore document collections. Electronic Imaging, 2017 (1): pp. 46–57, 2017.

[41]

D. Sacha, Y. Asano, C. Rohrdantz, F. Hamborg, D. Keim, B. Braun, and M. Butt. Self organizing maps for the visual analysis of pitch contours. Computational Linguistics, 2015.

[42]

D. Sacha, M. Kraus, J. Bernard, M. Behrisch, T. Schreck, Y. Asano, and D. A. Keim. Somflow: Guided exploratory cluster analysis with self-organizing maps and analytic provenance. Transactions on Visualization and Computer Graphics, 24 (1): pp. 120–130, 2018.

[43]

D. Sacha, L. Zhang, M. Sedlmair, J. A. Lee, J. Peltonen, D. Weiskopf, S. C. North, and D. A. Keim. Visual interaction with dimensionality reduction: A structured literature analysis. Transactions on Visualization and Computer Graphics, 23 (1): pp. 241–250, 2017.

Digital Library

[44]

A. Sarkar, M. Spott, A. Blackwell, and M. Jamnik. Visual discovery and model-driven explanation of time series patterns. In Visual Languages and Human-Centric Computing, pp. 78–86, 2016.

[45]

J. Sauro. Measuring usability with the system usability scale (sus), Accessed 05.11.2020.

[46]

T. Schreck, J. Bernard, T. von Landesberger, and J. Kohlhammer. Visual cluster analysis of trajectory data with interactive kohonen maps. Information Visualization, Palgrave Macmillan, 8 (1): pp. 14–29, 2009.

Digital Library

[47]

M. Sedlmair, A. Frank, T. Munzner, and A. Butz. Relex: Visualization for actively changing overlay network specifications. Visualization and Computer Graphics, 18: pp. 2729–2738, 2012.

Digital Library

[48]

M. Sedlmair, P. Isenberg, D. Baur, M. Mauerer, C. Pigorsch, and A. Butz. Cardiogram: Visual analytics for automotive engineers. In Human Factors in Computing Systems, pp. 1727–1736, 2011.

[49]

M. Sedlmair, M. Meyer, and T. Munzner. Design study methodology: Reflections from the trenches and the stacks. Visualization and Computer Graphics, 18: pp. 2431–2440, 2012.

Digital Library

[50]

B. Settles. Active learning literature survey. Computer Sciences Technical Report, University of Wisconsin–Madison, 2009.

[51]

B. Shneiderman. Inventing discovery tools: Combining information visualization with data mining. Information Visualization, 1 (1): pp. 5–12, 2002.

Digital Library

[52]

M. Someren, Y. Barnard, and J. Sandberg. The Think Aloud Method - A Practical Guide to Modelling CognitiveProcesses. Academic Press, 1994.

[53]

J. Suschnigg, B. Mutlu, A. Fuchs, V. Sabol, S. Thalmann, and T. Schreck. Exploration of anomalies in cyclic multivariate industrial time series data for condition monitoring. In Big Data Visual Exploration and Analytics, 2020.

[54]

M. Tory and T. Möller. Evaluating visualizations: Do expert reviews work?Computer graphics and applications, 25: pp. 8–11, 2005.

Digital Library

[55]

J. Vesanto. Som-based data visualization methods. Intelligent Data Analysis, 3 (2): pp. 111–126, 1999.

Digital Library

[56]

N. Weber, M. Waechter, S. C. Amend, S. Guthe, and M. Goesele. Rapid, detail-preserving image downscaling. Transactions on Graphics, 6, 2016.

[57]

N. Wilhelm, A. Vögele, R. Zsoldos, T. Licka, B. Krüger, and J. Bernard. Furyexplorer: Visual-interactive exploration of horse motion capture data. Visualization and Data Analysis, 2015.

[58]

D. Wu, X. Wang, J. Su, B. Tang, and S. Wu. A labeling method for financial time series prediction based on trends. Entropy, 22 (10), 2020.

[59]

W. Yang, X. Wang, J. Lu, W. Dou, and S. Liu. Interactive steering of hierarchical clustering. Transactions on Visualization and Computer Graphics, 2020.

[60]

J. Zhao, M. Karimzadeh, A. Masjedi, T. Wang, X. Zhang, M. M. Crawford, and D. S. Ebert. Featureexplorer: Interactive feature selection and exploration of regression models for hyperspectral images. In Visualization Conference, pp. 161–165, 2019.

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cover image IEEE Transactions on Visualization and Computer Graphics

IEEE Transactions on Visualization and Computer Graphics Volume 28, Issue 1

Jan. 2022

1190 pages

ISSN:1077-2626

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1077-2626 © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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IEEE Educational Activities Department

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Published: 01 January 2022

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Cited By

Grassini S(2024)The Hidden Psychological Costs of Virtual Work: Examining the Psychosocial Adverse Effects of Metaverse in the WorkplaceProceedings of the European Conference on Cognitive Ergonomics 202410.1145/3673805.3673834(1-6)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3673805.3673834
Coscia ASapers HDeutsch NKhurana MMagyar JParra SUtter DWipfler RCaress DMartin EPaduan JHendrie MLombeyda SMushkin HEndert ADavidoff SOrphan V(2024)DeepSee: Multidimensional Visualizations of Seabed EcosystemsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642001(1-16)Online publication date: 11-May-2024
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Wyss AMorgenshtern GHirsch-Hüsler ABernard J(2024)DaedalusData: Exploration, Knowledge Externalization and Labeling of Particles in Medical Manufacturing — A Design StudyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345632931:1(54-64)Online publication date: 23-Sep-2024
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