Computer Science ›› 2022, Vol. 49 ›› Issue (7): 89-99.doi: 10.11896/jsjkx.210900167

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Survey on Visualization Technology for Equipment Condition Monitoring

YANG Xiao, WANG Xiang-kun, HU Hao, ZHU Min   

  1. College of Computer Science,Sichuan University,Chengdu 610065,China
  • Received:2021-09-22 Revised:2022-03-14 Online:2022-07-15 Published:2022-07-12
  • About author:YANG Xiao,born in 1997,is a member of China Computer Federation.His main research interests include information visualization and visual analytics.
    ZHU Min,born in 1971,Ph.D,professor,Ph.D supervisor,is a senior member of China Computer Federation.Her main research interests include information visualization and visual analytics.
  • Supported by:
    Technology Projects Commissioned by Enterprises and Institutions(HG2020205).

Abstract: With the development of sensors and digital technology,more and more equipment and production environment are equipped with sensors and corresponding information systems.These sensors collect and transmit a lot of valuable data.The visua-lization technology for equipment condition monitoring,on the one hand,can integrate the professional experience of operators to objectively evaluate the operation status of the equipment,on the other hand,can intuitively explain the results of the data model,so as to carry out intelligent analysis of human-computer cooperation on the data.This paper summarizes the related research work of data visualization in equipment condition monitoring,and summarizes the general visualization process for equipment condition monitoring.Firstly,according to the data characteristics,the equipment condition monitoring data is divided into network data,spatiotemporal data,multidimensional data and statistical data.Then,on the basis of summarizing the general visualization process of the scene,four kinds of analysis tasks are summarized:condition monitoring,correlation analysis,abnormal reason reasoning and condition prediction.For each kind of analysis task,the visualization technology is summarized.Finally,the challenges and future trends of equipment condition monitoring visualization are summarized and prospected.

Key words: Abnormal reason reasoning, Correlation analysis, Equipment condition monitoring, Forecast analysis, Visualization technology

CLC Number: 

  • TP391
[1]ELSISI M,MAHMOUD K,LEHTONEN M,et al.Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing,Monitoring,and Securing Smart Meters[J/OL].Sensors,2021,21(2):487.https://doi.org/10.3390/s21020487.
[2]ZHOU Z G,SHI C,SHI L S,et al.A Survey on the Visual Analytics of Geospatial Data [J].Journal of Computer-Aided Design &Computer Graphics,2018 (5):747-763.
[3]ZHENG Y,WU W,CHEN Y,et al.Visual analytics in urbancomputing:An overview[J].IEEE Transactions on Big Data,2016,2(3):276-296.
[4]SENARATNE H,MUELLER M,BEHRISCH M,et al.Urban Mobility Analysis With Mobile Network Data:A Visual Analy-tics Approach[J].IEEE Transactions on Intelligent Transportation Systems,2018,19(5):1537-1546.
[5]CAO N,LIN C,ZHU Q,et al.Voila:Visual anomaly detection and monitoring with streaming spatiotemporal data[J].IEEE Transactions on Visualization and Computer Graphics,2017,24(1):23-33.
[6]ZHOU F,LIN X,LIU C,et al.A survey of visualization forsmart manufacturing[J].Journal of Visualization,2019,22(2):419-435.
[7]DUTTA S,CHEN C M,HEINLEIN G,et al.In situ distribution guided analysis and visualization of transonic jet engine simulations[J].IEEE Transactions on Visualization and Computer Graphics,2016,23(1):811-820.
[8]SENARATNE H,MUELLER M,BEHRISCH M,et al.Urban mobility analysis with mobile network data:A visual analytics approach[J].IEEE Transactions on Intelligent Transportation Systems,2017,19(5):1537-1546.
[9]ATZORI L,IERA A,MORABITO G.The internet of things:A survey[J].Computer networks,2010,54(15):2787-2805.
[10]STITZ H,GRATZL S,KRIEGER M,et al.CloudGazer:A divide-and-conquer approach to monitoring and optimizing cloud-based networks[C]//2015 IEEE Pacific Visualization Symposium(PacificVis).IEEE,2015:175-182.
[11]HADLAK S,SCHUMANN H,CAP C H,et al.Supporting the visual analysis of dynamic networks by clustering associated temporal attributes[J].IEEE Transactions on Visualization and Computer Graphics,2013,19(12):2267-2276.
[12]RODRÍGUEZ-MAZAHUA L,RODRÍGUEZ-ENRÍQUEZ C A,SÁNCHEZ-CERVANTES J L,et al.A general perspective of Big Data:applications,tools,challenges and trends[J].The Journal of Supercomputing,2016,72(8):3073-3113.
[13]MUKOSAKA S,KOIKE H.Integrated visualization system formonitoring security in large-scale local area network[C]//2007 6th International Asia-Pacific Symposium on Visualization.IEEE,2007:41-44.
[14]STEIGER M,BERNARD J,MITTELSTÄDT S,et al.Visualanalysis of time-series similarities for anomaly detection in sensor networks[J].Computer Graphics Forum,2014,33(3):401-410.
[15]XU K,WANG Y,YANG L,et al.Clouddet:Interactive visualanalysis of anomalous performances in cloud computing systems[J].IEEE Transactions on Visualization and Computer Graphics,2019,26(1):1107-1117.
[16]LI Z K.Condition monitoring and early warning of engine gearbox and gear processing equipment[D].Nanjing:Nanjing University of Technology,2019.
[17]CARD M.Readings in information visualization:using vision to think[M].San Francisco:Morgan Kaufmann,1999.
[18]SHI Y,LIU Y,TONG H,et al.Visual Analytics of Anomalous User Behaviors:A Survey [J].IEEE Transactions on Big Data,2022,8(2):377-396.
[19]BORKIN M A,VO A A,BYLINSKII Z,et al.What makes a visualization memorable?[J].IEEE Transactions on Visualization and Computer Graphics,2013,19(12):2306-2315.
[20]GREEN D,LINDAHL P,LEEB S,et al.Dashboard:Nonintrusive electromechanical fault detection and diagnostics[C]//2019 IEEE AUTOTESTCON.IEEE,2019:1-9.
[21]MATKOVIC K,HAUSER H,SAINITZER R,et al.Process visualization with levels of detail[C]//IEEE Symposium on Information Visualization(INFOVIS 2002).IEEE,2002:67-70.
[22]HARRIS R L.Information graphics:A comprehensive illustra-ted reference[M].New York:Oxford University Press,1999.
[23]XIA J,ZHANG Y,YE H,et al.SuPoolVisor:a visual analytics system for mining pool surveillance[J].Frontiers of InformationTechnology & Electronic Engineering,2020,21(4):507-523.
[24]WU W,ZHENG Y,QU H,et al.Boundaryseer:Visual analysis of 2d boundary changes[C]//2014 IEEE Conference on Visual Analytics Science and Technology (VAST).IEEE,2014:143-152.
[25]WU W,ZHENG Y,CHEN K,et al.A visual analytics approach for equipment condition monitoring in smart factories of process industry[C]//2018 IEEE Pacific Visualization Symposium (PacificVis).IEEE,2018:140-149.
[26]KIM J,LEE H,JEONG S,et al.Sound-based remote real-time multi-device operational monitoring system using a ConvolutionalNeural Network (CNN)[J].Journal of Manufacturing Systems,2021,58:431-441.
[27]ZHAO Y,LUO X,LIN X,et al.Visual analytics for electromagnetic situation awareness in radio monitoring and management[J].IEEE transactions on visualization and computer graphics,2019,26(1):590-600.
[28]XU P,MEI H,REN L,et al.ViDX:Visual diagnostics of assembly line performance in smart factories[J].IEEE Transactions on Visualization and Computer Graphics,2016,23(1):291-300.
[29]ZHAO Y,ZHOU F,FAN X.A real-time visualization framework for IDS alerts[C]//Proceedings of the 5th International Symposium on Visual Information Communication and Interaction.2012:11-17.
[30]SOKOLOVA A.New machinery condition monitoring technique based on multidimensional visualization of vibration s-discriminants[C]//10th European Conference Non-Destructive Testing 2010.2010:1779-1785.
[31]WEI D,LI C,SHAO H,et al.SensorAware:visual analysis of both static and mobile sensor information[J].Journal of Visuali-zation,2021,24(3):597-613.
[32]XIE C,XU W,MUELLER K.A visual analytics framework for the detection of anomalous call stack trees in high performance computing applications[J].IEEE Transactions on Visualization and Computer Graphics,2018,25(1):215-224.
[33]YANG F,SHAH S L,XIAO D,et al.Improved correlation ana-lysis and visualization of industrial alarm data[J].ISA transactions,2012,51(4):499-506.
[34]ZHANG Z H,ZHANG J P,CHEN D M,et al.Interactive Dimension Reordering in RadViz with Correlation Matrix [J].Pattern Recognition and Artificial Intelligence,2017,30(7):637-645.
[35]SHI L,LIAO Q,HE Y,et al.SAVE:Sensor anomaly visualization engine[C]//2011 IEEE Conference on Visual Analytics Science and Technology (VAST).IEEE,2011:201-210.
[36]RADOŠ S,SPLECHTNA R,MATKOVIĆ K,et al.Towardsquantitative visual analytics with structured brushing and linked statistics[J].Computer Graphics Forum,2016,35(3):251-260.
[37]KRUSKAL J B.Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis[J].Psychometrika,1964,29(1):1-27.
[38]SHYU M L,CHEN S C,SARINNAPAKORN K,et al.A Novel Anomaly Detection Scheme Based on Principal Component Classifier[C]//IEEE Foundations and New Directions of Data Mi-ning Workshop,in Conjunction with the Third IEEE InternationalConference on Data Mining(ICDM'03).IEEE,2003.
[39]VAN DER MAATEN L,HINTON G.Visualizing data usingt-SNE[J].Journal of Machine Learning Research,2008,9(11):2579-2605.
[40]GUO X,ZHAO Y,ZHAO Y.Research on condition monitoring of wind turbines data visualization based on random forest[C]//2016 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE).IEEE,2016:166-170.
[41]FUJIWARA T,SAKAMOTO N,NONAKA J,et al.A visual analytics framework for reviewing multivariate time-series data with dimensionality reduction[J].IEEE Transactions on Visuali-zation and Computer Graphics,2020,27(2):1601-1611.
[42]ZHOU Z,YE Z,LIU Y,et al.Visual analytics for spatial clusters of air-quality data[J].IEEE Computer Graphics and Applications,2017,37(5):98-105.
[43]XIAO Y,ZHENG K,LONAPALAWONG S,et al.EcoVis:visual analysis of industrial-level spatio-temporal correlations in electricity consumption[J].Frontiers of Computer Science,2022,16(2):1-11.
[44]MUELDER C,ZHU B,CHEN W,et al.Visual analysis of cloud computing performance using behavioral lines[J].IEEE Transactions on Visualization and Computer Graphics,2016,22(6):1694-1704.
[45]FRUCHTERMAN T M J,REINGOLD E M.Graph drawing by force-directed placement[J].Software:Practice and Experience,1991,21(11):1129-1164.
[46]MATKOVIĆ K,GRACˇANIN D,SPLECHTNA R,et al.Visual analytics for complex engineering systems:Hybrid visual stee-ring of simulation ensembles[J].IEEE Transactions on Visuali-zation and Computer Graphics,2014,20(12):1803-1812.
[47]ZHAO Y,WANG L,LI S,et al.A visual analysis approach for understanding durability test data of automotive products[J].ACM Transactions on Intelligent Systems and Technology (TIST),2019,10(6):1-23.
[48]ZHOU F,LIN X,LUO X,et al.Visually enhanced situationawareness for complex manufacturing facility monitoring in smart factories[J].Journal of Visual Languages & Computing,2018,44:58-69.
[49]KAUPP L,NAZEMI K,HUMM B.An industry 4.0-ready visualanalytics model for context-aware diagnosis in smart manufacturing[C]//2020 24th International Conference Information Visualisation (IV).IEEE,2020:350-359.
[50]GUO Y,GUO S,JIN Z,et al.Survey on Visual Analysis ofEvent Sequence Data[J].IEEE Transactions on Visualization and Computer Graphics,arXiv:2006.14291,2020.
[51]HERR D,KURZHALS K,ERTL T.Visual Analysis for Spatio-Temporal Event Correlation in Manufacturing[C]//Hawaii International Conference on System Sciences.2020.
[52]CHEN Y,XU P,REN L.Sequence synopsis:Optimize visualsummary of temporal event data[J].IEEE Transactions on Visualization and Computer Graphics,2017,24(1):45-55.
[53]HERR D,BECK F,ERTL T.Visual analytics for decomposing temporal event series of production lines[C]//2018 22nd International Conference Information Visualisation (IV).IEEE,2018:251-259.
[54]BUONO P,PLAISANT C,SIMEONE A,et al.Similarity-based forecasting with simultaneous previews:A river plot interface for time series forecasting[C]//2007 11th International Confe-rence Information Visualization (IV'07).IEEE,2007:191-196.
[55]YANG W,TAVNER P J,CRABTREE C J,et al.Wind turbine condition monitoring:technical and commercial challenges[J].Wind Energy,2014,17(5):673-693.
[56]TKACHEV G,FREY S,ERTL T.Local prediction models for spatiotemporal volume visualization[J].IEEE Transactions on Visualization and Computer Graphics,2019,2019,27(7):3091-3108.
[57]MACHADO C G,WINROTH M P,RIBEIRO DA SILVA E H D.Sustainable manufacturing in Industry 4.0:an emerging research agenda[J].International Journal of Production Research,2020,58(5):1462-1484.
[58]POSADA J,TORO C,BARANDIARAN I,et al.Visual computing as a key enabling technology for industrie 4.0 and industrial internet[J].IEEE Computer Graphics and Applications,2015,35(2):26-40.
[59]ZHANG P.Visualizing production planning data[J].IEEE Computer Graphics and Applications,1996,16(5):7-10.
[60]SYDOW A,KASSEL J F,ROHS M.Visualizing scheduling:Ahierarchical event-based approach on a tablet[C]//Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct.2015:728-734.
[61]JO J,HUH J,PARK J,et al.LiveGantt:Interactively visualizing a large manufacturing schedule[J].IEEE Transactions on Visualization and Computer Graphics,2014,20(12):2329-2338.
[62]WÖRNER M,ERTL T.Simulation-based visual layout planning in advanced manufacturing[C]//2013 46th Hawaii International Conference on System Sciences.IEEE,2013:1532-1541.
[63]SUN D,HUANG R,CHEN Y,et al.PlanningVis:A visual analytics approach to production planning in smart factories[J].IEEE Transactions on Visualization and Computer Graphics,2019,26(1):579-589.
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