research-article

A Visual Analysis Approach for Understanding Durability Test Data of Automotive Products

Authors:

Lei RenAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 10, Issue 6

Article No.: 70, Pages 1 - 23

https://doi.org/10.1145/3345640

Published: 12 December 2019 Publication History

Abstract

People face data-rich manufacturing environments in Industry 4.0. As an important technology for explaining and understanding complex data, visual analytics has been increasingly introduced into industrial data analysis scenarios. With the durability test of automotive starters as background, this study proposes a visual analysis approach for understanding large-scale and long-term durability test data. Guided by detailed scenario and requirement analyses, we first propose a migration-adapted clustering algorithm that utilizes a segmentation strategy and a group of matching-updating operations to achieve an efficient and accurate clustering analysis of the data for starting mode identification and abnormal test detection. We then design and implement a visual analysis system that provides a set of user-friendly visual designs and lightweight interactions to help people gain data insights into the test process overview, test data patterns, and durability performance dynamics. Finally, we conduct a quantitative algorithm evaluation, case study, and user interview by using real-world starter durability test datasets. The results demonstrate the effectiveness of the approach and its possible inspiration for the durability test data analysis of other similar industrial products.

Supplementary Material

zhao (zhao.zip)

Supplemental movie, appendix, image and software files for, A Visual Analysis Approach for Understanding Durability Test Data of Automotive Products

Download
15.63 MB

References

[1]

Zuriana Abu Bakar, Rosmayati Mohemad, Akbar Ahmad, and Mustafa Mat Deris. 2006. A comparative study for outlier detection techniques in data mining. In Proceedings of the 2006 IEEE Conference Cybernetics and Intelligent Systems. 1--6.

[2]

Bilal Alsallakh and Ren Liu. 2017. PowerSet: A comprehensive visualization of set intersections. IEEE Transactions on Visualization and Computer Graphics 23, 1 (Jan. 2017), 361--370.

Digital Library

[3]

Pei-Chann Chang, Chen-Hao Liu, and Chin-Yuan Fan. 2009. Data clustering and fuzzy neural network for sales forecasting: A case study in printed circuit board industry. Knowledge-Based Systems 22, 5 (May 2009), 344--355.

Digital Library

[4]

Siming Chen, Shuai Chen, Zhenhuang Wang, Jie Liang, Yadong Wu, and Xiaoru Yuan. 2018. D-Map+: Interactive visual analysis and exploration of ego-centric and event-centric information diffusion patterns in social media. ACM Transactions on Intelligent Systems and Technology 10 (Nov. 2018), 1--26.

[5]

Wei Chen, Zhaosong Huang, Feiran Wu, Minfeng Zhu, Huihua Guan, and Ross Maciejewski. 2018. VAUD: A visual analysis approach for exploring spatio-temporal urban data. IEEE Transactions on Visualization and Computer Graphics 24, 9 (Sept. 2018), 2636--2648.

[6]

Wei Chen, Jing Xia, Xumeng Wang, Yi Wang, Jun Chen, and Liang Chang. 2018. RelationLines: Visual reasoning of egocentric relations from heterogeneous urban data. ACM Transactions on Intelligent Systems and Technology 10 (Dec. 2018), 1--21.

[7]

Yanping Chen, Eamonn Keogh, Bing Hu, Nurjahan Begum, Anthony Bagnall, Abdullah Mueen, and Gustavo Batista. 2015. The UCR Time Series Classification Archive. Retrieved October 3, 2019 from https://www.cs.ucr.edu/&sim;eamonn/time_series_data/.

[8]

Rui Ding, Qiang Wang, Yingnong Dang, Qiang Fu, Haidong Zhang, and Dongmei Zhang. 2015. YADING: Fast clustering of large-scale time series data. Proceedings of the VLDB Endowment 8, 5 (Jan. 2015), 473--484.

Digital Library

[9]

Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD’96). 226--231. http://dl.acm.org/citation.cfm?id=3001460.3001507

Digital Library

[10]

Shunan Guo, Ke Xu, Rongwen Zhao, Gotz David, Hongyuan Zha, and Nan Cao. 2018. EventThread: Visual summarization and stage analysis of event sequence data. IEEE Transactions on Visualization and Computer Graphics 24, 1 (Jan. 2018), 56--65.

Digital Library

[11]

Alexander Hinneburg and Hans-Henning Gabriel. 2007. DENCLUE 2.0: Fast clustering based on kernel density estimation. In Proceedings of the 7th International Conference on Intelligent Data Analysis. 70--80.

[12]

Lars Huettenberger, Nils Feige, Achim Ebert, and Christoph Garth. 2015. Application of Pareto sets in quality control of series production in car manufacturing. In Proceedings of the 2015 IEEE Pacific Visualization Symposium (PacificVis’15). 135--139.

[13]

Thomas Hollt, Ahmed Magdy, Guoning Chen, Ganesh Gopalakrishnan, Ibrahim Hoteit, Charles D. Hansen, and Markus Hadwiger. 2013. Visual analysis of uncertainties in ocean forecasts for planning and operation of off-shore structures. In Proceedings of the 2013 IEEE Pacific Visualization Symposium (PacificVis’13). 59--66.

[14]

Jaemin Jo, Jaeseok Huh, Jonghun Park, Bohyoung Kim, and Jinwook Seo. 2014. LiveGantt: Interactively visualizing a large manufacturing schedule. IEEE Transactions on Visualization and Computer Graphics 20, 12 (Dec. 2014), 2329--2338.

[15]

Jim Davis, Thomas Edgar, James Porter, John Bernaden, and Michael Sarli. 2012. Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Computers and Chemical Engineering 47 (Dec. 2012), 145--156.

[16]

Johannes Kehrer, Harald Piringer, Wolfgang Berger, and Eduard Gröller. 2013. A model for structure-based comparison of many categories in small-multiple displays. IEEE Transactions on Visualization and Computer Graphics 19, 12 (Dec. 2013), 2287--2296.

Digital Library

[17]

Jay Lee, Hung-An Kao, and Shanhu Yang. 2014. Service innovation and smart analytics for Industry 4.0 and big data environment. Procedia CIRP 16 (2014), 3--8.

[18]

Shixia Liu, Weiwei Cui, Yingcai Wu, and Mengchen Liu. 2014. A survey on information visualization: Recent advances and challenges. Visual Computer 30, 12 (Dec. 2014), 1373--1393.

Digital Library

[19]

Yuxin Ma, Anthony K. H. Tung, Wei Wang, Xiang Gao, Zhigeng Pan, and Wei Chen. 2019. ScatterNet: A deep subjective similarity model for visual analysis of scatterplots. IEEE Transactions on Visualization and Computer Graphics (2019), 1--10.

[20]

Alexander Maier, Sebastian Schriegel, and Oliver Niggemann. 2017. Big Data and Machine Learning for the Smart Factory-Solutions for Condition Monitoring Diagnosis and Optimization. Springer International Publishing, Cham, Switzerland, 473--485.

[21]

Rainer Drath and Alexander Horch. 2014. Industrie 4.0: Hit or hype? [Industry Forum]. IEEE Industrial Electronics Magazine 8, 2 (June 2014), 56--58.

[22]

Rainer Splechtna, Kresimir Matkovic, Denis Gracanin, Mario Jelovic, and Helwig Hauser. 2015. Interactive visual steering of hierarchical simulation ensembles. In Proceedings of the 2015 IEEE Conference on Visual Analytics Science and Technology (VAST’15). 89--96.

[23]

Lei Ren, Lin Zhang, Lihui Wang, Fei Tao, and Xudong Chai. 2017. Cloud manufacturing: Key characteristics and applications. International Journal of Computer Integrated Manufacturing 30, 6 (April 2017), 501--515.

Digital Library

[24]

M. Sedlmair, W. Hintermaier, K. Stocker, T. Buring, and A. Butz. 2008. A dual-view visualization of in-car communication processes. In Proceedings of the International Conference on Information Visualisation. 157--162.

[25]

Michael Sedlmair, Petra Isenberg, Dominikus Baur, Michael Mauerer, Christian Pigorsch, and Andreas Butz. 2011. Cardiogram: Visual analytics for automotive engineers. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, NY, 1727--1736.

Digital Library

[26]

Ronghua Shi, Mengjie Yang, Ying Zhao, Fangfang Zhou, Wei Huang, and Sheng Zhang. 2016. A matrix-based visualization system for network traffic forensics. IEEE Systems Journal 10, 4 (Dec. 2016), 1350--1360.

[27]

Yang Shi, Chris Bryan, Sridatt Bhamidipati, Ying Zhao, Yaoxue Zhang, and Kwan-Liu Ma. 2018. MeetingVis: Visual narratives to assist in recalling meeting context and content. IEEE Transactions on Visualization and Computer Graphics 24, 6 (June 2018), 1918--1929.

[28]

Yang Shi, Ying Zhao, Fangfang Zhou, Ronghua Shi, and Yaoxue Zhang. 2018. A novel radial visualization of intrusion detection alerts. IEEE Computer Graphics and Applications 38, 6 (Nov. 2018), 83--95.

Digital Library

[29]

Stephan Pajer, Mark Streit, Thomas Tornsey-Weir, Florian Spechtenhauser, Torsten Moller, and Harald Piringer. 2017. WeightLifter: Visual weight space exploration for multi-criteria decision making. IEEE Transactions on Visualization and Computer Graphics 23, 1 (Jan. 2017), 611--620.

Digital Library

[30]

Guodao Sun, Yingcai Wu, Ronghua Liang, and Shixia Liu. 2013. A survey of visual analytics techniques and applications: State-of-the-art research and future challenges. Journal of Computer Science and Technology 28 (Sept. 2013), 852--867.

[31]

Johannes Weissenbock, Aleksandr Amirkhanov, Weimin Li, Andreas Reh, Alexander Amirkhanov, Meister Eduard Groller, Johann Kastner, and Christoph Heinzl. 2014. FiberScout: An interactive tool for exploring and analyzing fiber reinforced polymers. In Proceedings of the 2014 IEEE Pacific Visualization Symposium (PacificVis’14). 153--160.

Digital Library

[32]

Wenchao Wu, Yixian Zheng, Kaiyuan Chen, Xiangyu Wang, and Nan Cao. 2018. A visual analytics approach for equipment condition monitoring in smart factories of process industry. In Proceedings of the 2018 IEEE Pacific Visualization Symposium (PacificVis’18). 140--149.

[33]

Yingcai Wu, Nan Cao, David Gotz, Yap-Peng Tan, and Daniel A. Keim. 2016. A survey on visual analytics of social media data. IEEE Transactions on Multimedia 18, 11 (Nov. 2016), 2135--2148.

Digital Library

[34]

Jiazhi Xia, Fenjin Ye, Wei Chen, Yusi Wang, Weifeng Chen, Yuxin Ma, and Anthony K. H. Tung. 2018. LDSScanner: Exploratory analysis of low-dimensional structures in high-dimensional datasets. IEEE Transactions on Visualization and Computer Graphics 24, 1 (Jan. 2018), 236--245.

[35]

Cong Xie, Wei Chen, Xinxin Huang, Yueqi Hu, Scott Barlowe, and Jing Yang. 2014. VAET: A visual analytics approach for E-transactions time-series. IEEE Transactions on Visualization and Computer Graphics 20, 12 (Dec. 2014), 1743--1752.

[36]

Mingliang Xu, Hua Wang, Shili Chu, Yong Gan, Xiaoheng Jiang, Yafei Li, and Bing Zhou. 2019. Traffic simulation and visual verification in smog. ACM Transactions on Intelligent Systems and Technology 10, 1 (Jan. 2019), 1--17.

Digital Library

[37]

Panpan Xu, Honghui Mei, Liu Ren, and Wei Chen. 2017. ViDX: Visual diagnostics of assembly line performance in smart factories. IEEE Transactions on Visualization and Computer Graphics 23, 1 (Jan. 2017), 291--300.

Digital Library

[38]

Ying Zhao, Feng Luo, Minghui Chen, Yingchao Wang, Jiazhi Xia, Fangfang Zhou, Yunhai Wang, Yi Chen, and Wei Chen. 2019. Evaluating multi-dimensional visualizations for understanding fuzzy clusters. IEEE Transactions on Visualization and Computer Graphics 25, 1 (Jan. 2019), 12--21.

Digital Library

[39]

Ying Zhao, Yanmin She, Wenjiang Chen, Yutian Lu, Jiazhi Xia, Wei Chen, Junrong Liu, and Fangfang Zhou. 2018. EOD edge sampling for visualizing dynamic network via massive sequence view. IEEE Access 6 (June 2018), 53006--53018.

[40]

C. Zhou, P. E. Ramirez Lopez, J. Moreland, and B. Wu. 2011. Visualizing the future in steel manufacturing. Iron and Steel Technology 8, 1 (Jan. 2011), 37--50.

[41]

Fangfang Zhou, Xiaoru Lin, Chang Liu, Ying Zhao, Panpan Xu, Liu Ren, Tingmin Xue, and Lei Ren. 2019. A survey of visualization for smart manufacturing. Journal of Visualization 22, 2 (Jan. 2019), 419--435.

Digital Library

[42]

Fangfang Zhou, Xiaoru Lin, Xiaobo Luo, Ying Zhao, Yi Chen, Ning Chen, and Weihua Gui. 2018. Visually enhanced situation awareness for complex manufacturing facility monitoring in smart factories. Journal of Visual Languages and Computing 44 (Feb. 2018), 58--69.

[43]

Zhiguang Zhou, Linhao Meng, Cheng Tang, Ying Zhao, Zhiyong Guo, Miaoxin Hu, and Wei Chen. 2019. Visual abstraction of large scale geospatial origin-destination movement data. IEEE Transactions on Visualization and Computer Graphics 25, 1 (Jan. 2019), 43--53.

Digital Library

[44]

Zhiguang Zhou, Zhifei Ye, Yanan Liu, Fang Liu, Yubo Tao, and Weihua Su. 2017. Visual analytics for spatial clusters of air-quality data. IEEE Computer Graphics and Applications 37 (Jan. 2017), 98--105.

Digital Library

Cited By

Zhou FChen YZhu CJiang LLiao XZhong ZChen XChen YZhao Y(2024)Visual Analysis of Money Laundering in Cryptocurrency ExchangeIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.323168711:1(731-745)Online publication date: Mar-2024
https://doi.org/10.1109/TCSS.2022.3231687
Srivastava STheune MCatala A(2023)The Role of Lexical Alignment in Human Understanding of Explanations by Conversational AgentsProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584086(423-435)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581641.3584086
Motamedi ETkalcic MSzlávik Z(2023)Eudaimonic and Hedonic Qualities as Predictors of Music Videos’ Relevance to Users: A Human-Centric StudyAdjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3563359.3597415(44-49)Online publication date: 26-Jun-2023
https://dl.acm.org/doi/10.1145/3563359.3597415
Show More Cited By

Index Terms

A Visual Analysis Approach for Understanding Durability Test Data of Automotive Products
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Visual analytics

Recommendations

Application Massive Data Processing Platform for Smart Manufacturing Based on Optimization of Data Storage
The aim of smart manufacturing is to reduce manpower requirements of the production line by applying technology of huge amounts of data to the manufacturing industry. Smart manufacturing is also called Industry 4.0, and the platform for processing huge ...
Model-based Big Data Analytics-as-a-Service framework in smart manufacturing: A case study
Highlights
- A Big Data Analytics (BDA) approach to manage real-time data analytics.
- MBDAaaS ...
Abstract
Today, in a smart manufacturing environment based on the Industry 4.0 paradigm, people, technological infrastructure and machinery equipped with sensors can constantly generate and communicate a huge volume of data, also known as Big ...
Energy-efficient cyber-physical production network: Architecture and technologies
Highlights
- Highlighted energy-efficient manufacturing challenges in Industry 4.0 environment.
Abstract
Energy efficiency has become an integral aspect of today's manufacturing. As manufacturing activities become increasingly distributed while connected in the new era of manufacturing – Industry 4.0, energy-efficient manufacturing ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 10, Issue 6

Special Section on Intelligent Edge Computing for Cyber Physical and Cloud Systems and Regular Papers

November 2019

267 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3368406

Editor:
Yu Zheng
JD Finance, China

Issue’s Table of Contents

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 December 2019

Accepted: 01 July 2019

Revised: 01 July 2019

Received: 01 December 2018

Published in TIST Volume 10, Issue 6

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

National Key Research and Development Program of China
National Science Foundation of China
National Natural Science 8 Technology Fundamental Resources Investigation Program of China
Anhui Province Key Laboratory of Industry Safety and Emergency Technology
Natural Science Foundation of Hunan Province

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

19
Total Citations
View Citations
310
Total Downloads

Downloads (Last 12 months)33
Downloads (Last 6 weeks)4

Reflects downloads up to 27 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhou FChen YZhu CJiang LLiao XZhong ZChen XChen YZhao Y(2024)Visual Analysis of Money Laundering in Cryptocurrency ExchangeIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.323168711:1(731-745)Online publication date: Mar-2024
https://doi.org/10.1109/TCSS.2022.3231687
Srivastava STheune MCatala A(2023)The Role of Lexical Alignment in Human Understanding of Explanations by Conversational AgentsProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584086(423-435)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581641.3584086
Motamedi ETkalcic MSzlávik Z(2023)Eudaimonic and Hedonic Qualities as Predictors of Music Videos’ Relevance to Users: A Human-Centric StudyAdjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3563359.3597415(44-49)Online publication date: 26-Jun-2023
https://dl.acm.org/doi/10.1145/3563359.3597415
Geng LZhao HWang JKaushik AYuan SFeng W(2023)Deep-Reinforcement-Learning-Based Distributed Computation Offloading in Vehicular Edge Computing NetworksIEEE Internet of Things Journal10.1109/JIOT.2023.324701310:14(12416-12433)Online publication date: 15-Jul-2023
https://doi.org/10.1109/JIOT.2023.3247013
Islam MAhmed MBarua SBegum S(2022)A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and TasksApplied Sciences10.3390/app1203135312:3(1353)Online publication date: 27-Jan-2022
https://doi.org/10.3390/app12031353
Degas AIslam MHurter CBarua SRahman HPoudel MRuscio DAhmed MBegum SRahman MBonelli SCartocci GDi Flumeri GBorghini GBabiloni FAricó P(2022)A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research TrajectoryApplied Sciences10.3390/app1203129512:3(1295)Online publication date: 26-Jan-2022
https://doi.org/10.3390/app12031295
Freitas LLelli V(2022)Using Machine Learning on Testing IoT Applications: a systematic mappingProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3539637.3558049(348-358)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3539637.3558049
Kang HKocielnik RHead AYang JLatzke MKittur AWeld DDowney DBragg J(2022)From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social NetworksProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517470(1-23)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517470
Zhao YGe LXie HBai GZhang ZWei QLin YLiu YZhou F(2022)ASTF: Visual Abstractions of Time-Varying Patterns in Radio SignalsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.3209469(1-11)Online publication date: 2022
https://doi.org/10.1109/TVCG.2022.3209469
Wang YZhu ZWang LSun GLiang R(2022)Visualization and visual analysis of multimedia data in manufacturing: A surveyVisual Informatics10.1016/j.visinf.2022.09.0016:4(12-21)Online publication date: Dec-2022
https://doi.org/10.1016/j.visinf.2022.09.001
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents