Contents The world we live in today is powered by systems, machines, and processes that are the results of engineering. Electric motors are at the basis of many industrial and domestic applications, converting electrical energy into various forms of mechanical energy in a highly efficient manner. Their performance affects key indicators such as consumption, lifetime, or performance of the mechanical process. This places high demands on the validated design of electric motors.
Abstract:
End-of-line tests and defect detection are vital for ensuring the reliability of electric motors. However, automated defect detection methods (e.g., data-driven approache...Show MoreMetadata
Abstract:
End-of-line tests and defect detection are vital for ensuring the reliability of electric motors. However, automated defect detection methods (e.g., data-driven approaches) face challenges due to the limited availability of real data from failed motors. Simulated data, though beneficial, lacks the complexity of real motors, impacting the performance of these methods when applied to actual observations. To tackle this challenge, we introduce a visual analysis tool designed to facilitate the analysis of measured and simulated data, presented in the form of time series data. This tool helps identify domain-invariant features and evaluate simulation data accuracy, assisting in selecting training data for reliable automated defect detection in real-world scenarios. The main contribution of this work is a design proposal based on visual design principles, specifically tailored to address the unique requirements of electric motor professionals. The visual design is validated by findings from a think-aloud study with specialized engineers.
Published in: IEEE Computer Graphics and Applications ( Volume: 44, Issue: 4, July-Aug. 2024)
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1.
E. Marth, P. Zorn, F. Schmid, S. Masoudian, K. Koutini and W. Amrhein, "Simulation-assisted training of neural networks for condition monitoring of electrical drives: Approach and proof of concept", Proc. IKMT GMM/ETG- Symp., pp. 1-7, 2022.
2.
M. Pacas and S. Villwock, "Development of an expert system for identification commissioning and monitoring of drives", Proc. 13th Int. Power Electron. Motion Control Conf., pp. 2248-2253, 2008.
3.
S. Zhang, S. Zhang, B. Wang and T. G. Habetler, "Deep learning algorithms for bearing fault diagnostics–A comprehensive review", IEEE Access, vol. 8, pp. 29857-29881, 2020.
4.
S. B. Lee et al., "Condition monitoring of industrial electric machines: State of the art and future challenges", IEEE Ind. Electron. Mag., vol. 14, no. 4, pp. 158-167, Dec. 2020.
5.
M. Kande, A. J. Isaksson, R. Thottappillil and N. Taylor, "Rotating electrical machine condition monitoring automation–A review", Machines, vol. 5, no. 4, 2017.
6.
A. Rassõlkin, V. Rjabtšikov, T. Vaimann, A. Kallaste, V. Kuts and A. Partyshev, "Digital twin of an electrical motor based on empirical performance model", Proc. 11th Int. Conf. Elect. Power Drive Syst., pp. 1-4, 2020.
7.
J. Zhao, F. Chevalier and R. Balakrishnan, "Kronominer: Using multi-foci navigation for the visual exploration of time-series data", Proc. SIGCHI Conf. Hum. Factors Comput. Syst., pp. 1737-1746, 2011.
8.
D. Ceneda, T. Gschwandtner, S. Miksch and C. Tominski, "Guided visual exploration of cyclical patterns in time-series", Proc. IEEE Symp. Vis. Data Sci., 2018.
9.
M. C. Hao, U. Dayal, D. A. Keim and T. Schreck, "Multi-resolution techniques for visual exploration of large time-series data" in Proc. EUROVIS, pp. 27-34, 2007.
10.
Y. Shi et al., "Supporting guided exploratory visual analysis on time series data with reinforcement learning", IEEE Trans. Vis. Comput. Graphics, vol. 30, no. 1, pp. 1172-1182, Jan. 2024.
11.
N. Andrienko, G. Andrienko and G. Shirato, "Episodes and topics in multivariate temporal data" in Computer Graphics Forum, Hoboken, NJ, USA:Wiley Online Library, vol. 42, 2023.
12.
T. Fujiwara, "A visual analytics framework for reviewing multivariate time-series data with dimensionality reduction", IEEE Trans. Vis. Comput. Graphics, vol. 27, no. 2, pp. 1601-1611, Feb. 2021.
13.
J. Eirich et al., "ManEx: The visual analysis of measurements for the assessment of errors in electrical engines", IEEE Comput. Graphics Appl., vol. 42, no. 2, pp. 68-80, Mar./Apr. 2022.
14.
R. Silva, A. Salimi, M. Li, A. R. Freitas, F. G. Guimarães and D. A. Lowther, "Visualization and analysis of tradeoffs in many-objective optimization: A case study on the interior permanent magnet motor design", IEEE Trans. Magn., vol. 52, no. 3, Mar. 2016.
15.
M. Friendly and D. Denis, "The early origins and development of the scatterplot", J. Hist. Behav. Sci., vol. 41, no. 2, pp. 103-130, 2005.
16.
T. Büring, J. Gerken and H. Reiterer, "User interaction with scatterplots on small screens-a comparative evaluation of geometric-semantic zoom and fisheye distortion", IEEE Trans. Vis. Comput. Graphics, vol. 12, no. 5, pp. 829-836, Sep./Oct. 2006.
17.
E. A. Bier, M. C. Stone, K. Pier, W. Buxton and T. D. DeRose, "Toolglass and magic lenses: The see-through interface", Proc. 20th Annu. Conf. Comput. Graphics Interactive Techn., pp. 73-80, 1993.
19.
G. Weidenholzer, S. Silber, G. Jungmayr, G. Bramerdorfer, H. Grabner and W. Amrhein, "A flux-based PMSM motor model using RBF interpolation for time-stepping simulations", Proc. IEEE Int. Electric Mach. Drives Conf., pp. 1418-1423, 2013.
20.
S. Silber, W. Koppelstätter, G. Weidenholzer, G. Segon and G. Bramerdorfer, "Reducing development time of electric machines with SyMSpace", Proc. IEEE 8th Int. Electric Drives Prod. Conf., pp. 1-5, 2018.
21.
S. Nandi, H. Toliyat and X. Li, "Condition monitoring and fault diagnosis of electrical motors–a review", IEEE Trans. Energy Convers., vol. 20, no. 4, pp. 719-729, Dec. 2005.
22.
A. G. Asuero, A. Sayago and A. González, "The correlation coefficient: An overview", Crit. Rev. Anal. Chem., vol. 36, no. 1, pp. 41-59, 2006.
23.
E. R. Tufte, The Visual Display of Quantitative Information, Cheshire, CT, USA:Graphics Press, 2001.
24.
S. Few, Information Dashboard Design: Displaying Data for At-a-glance Monitoring, Berkeley, CA, USA:Analytics Press, 2013.
25.
A. Cairo, The Functional Art: An Introduction to Information Graphics and Visualization, Indianapolis, IN, USA:New Riders Publishing, 2012.
26.
J. Brooke, "Sus: A ’quick and dirty’ usability", Usability Eval. Ind., vol. 189, no. 3, pp. 189-194, 1996.
27.
J. Sauro, "Measuring usability with the system usability scale (SUS)", MeasuringU, Feb. 2011.
28.
A. Joshi, S. Kale, S. Chandel and D. K. Pal, "Likert scale: Explored and explained", Brit. J. Appl. Sci. Technol., vol. 7, no. 4, pp. 396-403, 2015.
29.
J. Suschnigg, B. Mutlu, G. Koutroulis, V. Sabol, S. Thalmann and T. Schreck, "Visual exploration of anomalies in cyclic time series data with matrix and glyph representations", Big Data Res., vol. 26, 2021.
