April 2017, 2(2): 127-139. doi: 10.3934/bdia.2017002

Big data collection and analysis for manufacturing organisations

1. 

Department of Mechanical Engineering, Hauz Khas, Indian Institute of Technology Delhi, New Delhi, 110016, India

2. 

Faculty of Applied Science, Department of Computing, Engineering and Technology, Industry Centre, Hylton Riverside, Sunderland, UK

3. 

Department of Informatics, Linnaeus University, SE-351 95 Växjö, Sweden

4. 

VTT Technical Research Centre of Finland, P.O.Box 1000, FI-02044 VTT, Finland

Published  April 2017

Data mining applications are becoming increasingly important for the wide range of manufacturing and maintenance processes. During daily operations, large amounts of data are generated. This large volume and variety of data, arriving at a greater velocity has its own advantages and disadvantages. On the negative side, the abundance of data often impedes the ability to extract useful knowledge. In addition, the large amounts of data stored in often unconnected databases make it impractical to manually analyse for valuable decision-making information. However, an advent of new generation big data analytical tools has started to provide large scale benefits for the organizations. The paper examines the possible data inputs from machines, people and organizations that can be analysed for maintenance. Further, the role of big data within maintenance is explained and how, if not managed correctly, big data can create problems rather than provide solutions. The paper highlights the need to have advanced mining techniques to enable conversion of data into information in an acceptable time frame and to have modern analytical tools to extract value from the big datasets.

Citation: Pankaj Sharma, David Baglee, Jaime Campos, Erkki Jantunen. Big data collection and analysis for manufacturing organisations. Big Data & Information Analytics, 2017, 2 (2) : 127-139. doi: 10.3934/bdia.2017002
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[2]

A. Koronios, S. Lin and J. Gao, A data quality model for asset management in engineering organisations, Proceedings of the 10th International Conference on Information Quality, Massachusetts Institute of Technology, Cambridge, USA, 2005.

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G. Gilliland, S. K. Barger, V. Bhatia and R. Nicol, Creating value through data integrity: A pragmatic approach, BCG Perspectives, (2011), Available at http://www.bcgindia.com/documents/file83320.pdf.

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J. S. RaoM. Zubair and C. Rao, Condition monitoring of power plants through the Internet, Integrated Manufacturing Systems, 14 (2003), 508-517. doi: 10.1108/09576060310491379.

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O. Prakash, Asset management through condition monitoring -How it may go wrong: A case study, Proceedings of the 1st World Congress on Engineering Asset Management, (WCEAM) 2006, Gold Coast, Queensland, Australia, July 11–14,2006.

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S. A. Kalogirou, Artificial intelligence for the modeling and control of combustion processes: A review, Progress in Energy and Combustion Science, 29 (2003), 515-566. doi: 10.1016/S0360-1285(03)00058-3.

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K. Warwick, A. O. Ekwue and R. Aggarwal, Artificial Intelligence Techniques in Power Systems, Institution of Electrical Engineers, Stevenage, UK, 1997. doi: 10.1049/PBPO022E.

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K. Wang, Intelligent Condition Monitoring and Diagnosis System a Computational Intelligent Approach, Frontiers in Artificial Intelligence and Applications, 93,2003.

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M. RaoH. Yang and H. Yang, Integrated distributed intelligent system for incident reporting in DMI pulp mill, success and failures of knowledge-based systems in real-world applications, Proceedings of the First International Conference, (1996), 169-178.

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M. RaoJ. Zhou and H. Yang, Architecture of integrated distributed intelligent multimedia system for on-line real-time process monitoring, SMC'98 Conference Proceedings, 1998 IEEE International Conference on Systems, Man, and Cybernetics, 2 (1998), 1411-16.

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K. M. ReichardM. Van Dyke and K. Maynard, Application of sensor fusion and signal classification techniques in a distributed machinery condition monitoring system, Proceedings of SPIE -The International Society for Optical Engineering, 4051 (2000), 329-336. doi: 10.1117/12.381646.

[14]

J. Campos and O. Prakash, Information and communication technologies in condition monitoring and maintenance, in Dolgui, A., Morel, G and Pereira, C. E. (Eds. ) Information Control Problems in Manufacturing, Elsevier, 39 (2006), 3-8. doi: 10.3182/20060517-3-FR-2903.00003.

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N. Taleb, Antifragile: How to Live in a World We Don't Understand, Penguin Books Limited, 2012.

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[31]

H. V. JagadishJ. GehrkeA. LabrinidisY. PapakonstantinouJ. M. PatelR. Ramakrishnan and C. Shahabi, Big data and its technical challenges, Communications Of The ACM, 57 (2014), 86-94. doi: 10.1145/2611567.

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U. KumarD. GalarA. ParidaC. Stenström and L. Berges, Maintenance performance metrics: A state-of-the-art review, Journal of Quality in Maintenance Engineering, 19 (2013), 233-277.

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W. J. Orlikowski and S. R. Barley, Technology and institutions: What can research on information technology and research on organizations learn from each other?, MIS Quarterly, 25 (2001), 145-165. doi: 10.2307/3250927.

[34]

S. Rogers, Big data is scaling bi and analytics, Available at http://www.informationmanagement.com/issues/21-5/big-data-is-scaling-bi-and-analytics-10021093-1.html, 2011.

[35]

J. FanF. Han and H. Liu, Challenges of big data analysis, National Science Review, 1 (2014), 293-314. doi: 10.1093/nsr/nwt032.

[36]

X. WuX. ZhuG.-Q. Wu and W. Ding, Data mining with big data, IEEE Transactions on Knowledge and Data Engineering, 26 (2014), 97-107.

[37]

J. C. HsiehA. H. Li and C. C. Yang, Mobile, cloud, and big data computing: Contributions, challenges, and new directions in telecardiology, International Journal of Environmental Research and Public Health, 10 (2013), 6131-6153. doi: 10.3390/ijerph10116131.

[38]

ISACA, Generating Value From Big Data Analytics, White Paper, Retrieved from (http://www.isaca.org), 2014.

[39]

J. BollenH. Mao and X. Zeng, Twitter mood predicts the stock market, Journal of Computational Science, 2 (2011), 1-8. doi: 10.1016/j.jocs.2010.12.007.

[40]

A. Gundami and M. Haider, Beyond the hype: Big data concepts, methods, and analytics, International Journal of Information Management, 35 (2015), 137-144. doi: 10.1016/j.ijinfomgt.2014.10.007.

[41]

P. Oborski, Man-machine interactions in advanced manufacturing systems, The International Journal of Advanced Manufacturing Technology, 23 (2004), 227-232. doi: 10.1007/s00170-003-1574-5.

[42]

J. Horák, I. Ivan, T. Inspektor and J. Tesla, Sparse big data problem: A case study of czech graffiti crimes, In: Ivan I., Singleton A., Horák J., Inspektor T. (eds) The Rise of Big Spatial Data, Lecture Notes in Geoinformation and Cartography, Springer, 2017.

[43]

Y. YanL. J. Chen and Z. Zhang, Error bounded sampling for analytics on big sparse data, Proceedings of the VLDB Endowment, 7 (2014), 1508-1519. doi: 10.14778/2733004.2733022.

[44]

P. K. KumarP. C. RaoR. ChangalaT. J. Rao and P. H. Shankar, Data mining challenges with big data, International Journal for Research in Applied Science & Engineering Technology (IJRASET), 3 (2015), 148-150.

[45]

R. LongadgeS. S. Dongre and L. Malik, Class imbalance problem in data mining: Review, International Journal of Computer Science and Network (IJCSN), 2 (2013).

[46]

H. He and E. A. Garcia, Learning from imbalanced data, IEEE Transactions on Knowledge and Data Engineering, 21 (2009), 1263-1284.

[47]

Y. SunA. K. C. Wong and M. S. Kamel, Classification of imbalanced data: A review, International Journal of Pattern Recognition and Artificial Intelligence, 23 (2009), 687-719. doi: 10.1142/S0218001409007326.

[48]

G. M. Weiss, Mining with rarity: A unifying framework, SIGKDD Explorations, 6 (2004), 7-19. doi: 10.1145/1007730.1007734.

[49]

G. Widmer and M. Kubat, Learning in the presence of concept drift and hidden contexts, Machine Learning, 23 (1996), 69-101. doi: 10.1007/BF00116900.

[50]

P. ZhangX. Zhu and Y. Shi, Categorizing and mining concept drifting data streams, the 14th ACM SIGKDD International Conference On Knowledge Discovery And Data Mining, (2008), 812-820. doi: 10.1145/1401890.1401987.

[51]

M. B. Chandak, Role of big data in classification and novel class detection in data streams, Journal of Big Data, 3 (2016), 1-9.

[52]

I. ZliobaiteM. Pechenizkiy and J. Gama, An overview of concept drift applications, Big Data Analysis: New Algorithms for a New Society, 16 (2015), 91-114.

show all references

References:
[1]

A. V. Levitan and T. C. Redman, Data as a resource: Properties, implications and prescriptions, Sloan Management Review, 40 (1998), 89-101.

[2]

A. Koronios, S. Lin and J. Gao, A data quality model for asset management in engineering organisations, Proceedings of the 10th International Conference on Information Quality, Massachusetts Institute of Technology, Cambridge, USA, 2005.

[3]

G. Gilliland, S. K. Barger, V. Bhatia and R. Nicol, Creating value through data integrity: A pragmatic approach, BCG Perspectives, (2011), Available at http://www.bcgindia.com/documents/file83320.pdf.

[4]

J. S. RaoM. Zubair and C. Rao, Condition monitoring of power plants through the Internet, Integrated Manufacturing Systems, 14 (2003), 508-517. doi: 10.1108/09576060310491379.

[5]

O. Prakash, Asset management through condition monitoring -How it may go wrong: A case study, Proceedings of the 1st World Congress on Engineering Asset Management, (WCEAM) 2006, Gold Coast, Queensland, Australia, July 11–14,2006.

[6]

S. A. Kalogirou, Artificial intelligence for the modeling and control of combustion processes: A review, Progress in Energy and Combustion Science, 29 (2003), 515-566. doi: 10.1016/S0360-1285(03)00058-3.

[7]

S. H. Liao, Expert system methodologies and applications -A decade review from 1995 to 2004, Expert Systems with Applications, 28 (2005), 93-103.

[8]

K. Warwick, A. O. Ekwue and R. Aggarwal, Artificial Intelligence Techniques in Power Systems, Institution of Electrical Engineers, Stevenage, UK, 1997. doi: 10.1049/PBPO022E.

[9]

K. Wang, Intelligent Condition Monitoring and Diagnosis System a Computational Intelligent Approach, Frontiers in Artificial Intelligence and Applications, 93,2003.

[10]

M. RaoH. Yang and H. Yang, Integrated distributed intelligent system for incident reporting in DMI pulp mill, success and failures of knowledge-based systems in real-world applications, Proceedings of the First International Conference, (1996), 169-178.

[11]

M. RaoJ. Zhou and H. Yang, Architecture of integrated distributed intelligent multimedia system for on-line real-time process monitoring, SMC'98 Conference Proceedings, 1998 IEEE International Conference on Systems, Man, and Cybernetics, 2 (1998), 1411-16.

[12]

M. RaoJ. Zhou and H. Yang, Integrated distributed intelligent system architecture for incidents monitoring and diagnosis, Computers in Industry, 37 (1998), 143-151. doi: 10.1016/S0166-3615(98)00090-6.

[13]

K. M. ReichardM. Van Dyke and K. Maynard, Application of sensor fusion and signal classification techniques in a distributed machinery condition monitoring system, Proceedings of SPIE -The International Society for Optical Engineering, 4051 (2000), 329-336. doi: 10.1117/12.381646.

[14]

J. Campos and O. Prakash, Information and communication technologies in condition monitoring and maintenance, in Dolgui, A., Morel, G and Pereira, C. E. (Eds. ) Information Control Problems in Manufacturing, Elsevier, 39 (2006), 3-8. doi: 10.3182/20060517-3-FR-2903.00003.

[15]

J. Campos, Survey paper: Development in the application of ICT in condition monitoring and maintenance, Computers in Industry, 60 (2009), 1-20. doi: 10.1016/j.compind.2008.09.007.

[16]

K. P. Sycara, MultiAgent Systems, AI Magazine, 19 (1998).

[17]

J. Q. FengD. P. BuseQ. H. Wu and J. Fitch, A multi-agent based intelligent monitoring system for power transformers in distributed substations, International Conference on Power System Technology Proceedings, 3 (2002), 1962-1965. doi: 10.1109/ICPST.2002.1067876.

[18]

A. C. Weaver, The internet and the world wide web, 23 rd International Conference on Industrial Electronics, Control and Instrumentation, 4 (1997), 1529-1540. doi: 10.1109/IECON.1997.664910.

[19]

D. Stenmark, Designing the new intranet, Gothenburg Studies in Informatics, Report 21, March 2002.

[20]

M. D. Assunção, R. N. Calheiros, S. Bianchi, M. A. S. Netto and R. Buyya, Big data computing and clouds: Trends and future directions, Journal of Parallel and Distributed Computing, Special Issue on Scalable Systems for Big Data Management and Analytics, (2015), 79-80, 3-15. doi: 10.1016/j.jpdc.2014.08.003.

[21]

B. Xu and S. Kumar, Big Data Analytics Framework For System Health Monitoring, Presented at the 2015 IEEE International Congress on Big Data (BigData Congress), IEEE Computer Society, 2015. doi: 10.1109/BigDataCongress.2015.66.

[22]

E. FumeoL. Oneto and D. Anguita, Condition based maintenance in railway transportation systems based on big data streaming analysis, Procedia Computer Science, 53 (2015), 437-446. doi: 10.1016/j.procs.2015.07.321.

[23]

A. Mohamed, M. S. Hamdi and S. Tahar, A machine learning approach for big data in oil and gas pipelines, Presented at the 2015 International Conference on Future Internet of Things and Cloud (FiCloud), 2015 International Conference on Open and Big Data (OBD), IEEE Computer Society, 2015. doi: 10.1109/FiCloud.2015.54.

[24]

A. Nunez, J. Hendriks, L. Zili, B. De Schutterand and R. Dollevoet, Facilitating maintenance decisions on the dutch railways using big data: The ABA case study, Presented at the 2014 IEEE International Conference on Big Data (Big Data), IEEE. 2014. doi: 10.1109/BigData.2014.7004431.

[25]

A. Parida and U. Kumar, Managing information is key to maintenance effectiveness, in Proceedings of Intelligent Maintenance System, Arles, France, 15-17 July, 2004.

[26]

P. Soderholm, Continuous Improvement of Complex Technical System: Aspects of Stakeholder Requirements and System Functions, Licentiate Thesis, Division of Quality and Environmental Management, Lulea University of Technology, Lulea, 2003.

[27]

G. ChenS. Wua and Y. Wang, The evolvement of big data systems: From the perspective of an information security application, Big Data Research, 2 (2015), 65-73. doi: 10.1016/j.bdr.2015.01.002.

[28]

W. Fan and A. Bifet, Mining big data: Current status, and forecast to the future, SIGKDD Explorations, 14 (2012), 1-5. doi: 10.1145/2481244.2481246.

[29]

N. Taleb, Antifragile: How to Live in a World We Don't Understand, Penguin Books Limited, 2012.

[30]

A. Parida, Role of condition monitoring and performance measurements in asset productivity enhancement, 20th International Conference on Condition Monitoring and Diagnostic Engineering Management, Faro, Portugal, 2007.

[31]

H. V. JagadishJ. GehrkeA. LabrinidisY. PapakonstantinouJ. M. PatelR. Ramakrishnan and C. Shahabi, Big data and its technical challenges, Communications Of The ACM, 57 (2014), 86-94. doi: 10.1145/2611567.

[32]

U. KumarD. GalarA. ParidaC. Stenström and L. Berges, Maintenance performance metrics: A state-of-the-art review, Journal of Quality in Maintenance Engineering, 19 (2013), 233-277.

[33]

W. J. Orlikowski and S. R. Barley, Technology and institutions: What can research on information technology and research on organizations learn from each other?, MIS Quarterly, 25 (2001), 145-165. doi: 10.2307/3250927.

[34]

S. Rogers, Big data is scaling bi and analytics, Available at http://www.informationmanagement.com/issues/21-5/big-data-is-scaling-bi-and-analytics-10021093-1.html, 2011.

[35]

J. FanF. Han and H. Liu, Challenges of big data analysis, National Science Review, 1 (2014), 293-314. doi: 10.1093/nsr/nwt032.

[36]

X. WuX. ZhuG.-Q. Wu and W. Ding, Data mining with big data, IEEE Transactions on Knowledge and Data Engineering, 26 (2014), 97-107.

[37]

J. C. HsiehA. H. Li and C. C. Yang, Mobile, cloud, and big data computing: Contributions, challenges, and new directions in telecardiology, International Journal of Environmental Research and Public Health, 10 (2013), 6131-6153. doi: 10.3390/ijerph10116131.

[38]

ISACA, Generating Value From Big Data Analytics, White Paper, Retrieved from (http://www.isaca.org), 2014.

[39]

J. BollenH. Mao and X. Zeng, Twitter mood predicts the stock market, Journal of Computational Science, 2 (2011), 1-8. doi: 10.1016/j.jocs.2010.12.007.

[40]

A. Gundami and M. Haider, Beyond the hype: Big data concepts, methods, and analytics, International Journal of Information Management, 35 (2015), 137-144. doi: 10.1016/j.ijinfomgt.2014.10.007.

[41]

P. Oborski, Man-machine interactions in advanced manufacturing systems, The International Journal of Advanced Manufacturing Technology, 23 (2004), 227-232. doi: 10.1007/s00170-003-1574-5.

[42]

J. Horák, I. Ivan, T. Inspektor and J. Tesla, Sparse big data problem: A case study of czech graffiti crimes, In: Ivan I., Singleton A., Horák J., Inspektor T. (eds) The Rise of Big Spatial Data, Lecture Notes in Geoinformation and Cartography, Springer, 2017.

[43]

Y. YanL. J. Chen and Z. Zhang, Error bounded sampling for analytics on big sparse data, Proceedings of the VLDB Endowment, 7 (2014), 1508-1519. doi: 10.14778/2733004.2733022.

[44]

P. K. KumarP. C. RaoR. ChangalaT. J. Rao and P. H. Shankar, Data mining challenges with big data, International Journal for Research in Applied Science & Engineering Technology (IJRASET), 3 (2015), 148-150.

[45]

R. LongadgeS. S. Dongre and L. Malik, Class imbalance problem in data mining: Review, International Journal of Computer Science and Network (IJCSN), 2 (2013).

[46]

H. He and E. A. Garcia, Learning from imbalanced data, IEEE Transactions on Knowledge and Data Engineering, 21 (2009), 1263-1284.

[47]

Y. SunA. K. C. Wong and M. S. Kamel, Classification of imbalanced data: A review, International Journal of Pattern Recognition and Artificial Intelligence, 23 (2009), 687-719. doi: 10.1142/S0218001409007326.

[48]

G. M. Weiss, Mining with rarity: A unifying framework, SIGKDD Explorations, 6 (2004), 7-19. doi: 10.1145/1007730.1007734.

[49]

G. Widmer and M. Kubat, Learning in the presence of concept drift and hidden contexts, Machine Learning, 23 (1996), 69-101. doi: 10.1007/BF00116900.

[50]

P. ZhangX. Zhu and Y. Shi, Categorizing and mining concept drifting data streams, the 14th ACM SIGKDD International Conference On Knowledge Discovery And Data Mining, (2008), 812-820. doi: 10.1145/1401890.1401987.

[51]

M. B. Chandak, Role of big data in classification and novel class detection in data streams, Journal of Big Data, 3 (2016), 1-9.

[52]

I. ZliobaiteM. Pechenizkiy and J. Gama, An overview of concept drift applications, Big Data Analysis: New Algorithms for a New Society, 16 (2015), 91-114.

Figure 1.  A Conceptual architecture of complex data analysis for maintenance decisions (Adapted from Health management of Complex technical systems, [26]
Figure 2.  Old traditional gearbox opened for checking the wear of gear teeth
Figure 3.  Big Data in Asset Management
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