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Erschienen in: Wireless Personal Communications 4/2018

20.04.2017

A Study on Prediction Model of Equipment Failure Through Analysis of Big Data Based on RHadoop

verfasst von: Jin-Hee Ku

Erschienen in: Wireless Personal Communications | Ausgabe 4/2018

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Abstract

With the development of the internet of things, which is widely applied not only to everyday objects but also to industrial areas, the production of big data is accelerating. To provide intelligent services without human intervention in the internet of things environment, intelligent communication between objects becomes the key, and since the failure of the mechanical equipment attached to the sensor causes malfunction of the object and product failure, big data analysis to predict equipment failure is becoming more important. The purpose of this study is to propose a model for predicting mechanical equipment failure from various sense data collected in the manufacturing process. This study constructed a RHadoop-based big data platform to distribute a large amount of datasets for research, and performed logistic regression modeling to predict the main variables causing the failure from various collected variables. As a result of the study, the main variables in the manufacturing process that cause equipment failure were derived from the collected sense data, and the fitness and performance evaluation for the prediction model were made using the ROC curve. As a result of the performance evaluation of the prediction model, the ROC curve showed a fairly high prediction accuracy with AUC close to 1. The results of this study are expected to be applicable to the prediction of malfunctions, product failure, or abnormal communication between objects due to miscellaneous product faults in our daily lives in the internet of things environment.

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Literatur
1.
Zurück zum Zitat Yun, J. R. (2016). 4th industrial revolution and soft power. TTA Journal, 167, 4–7. Yun, J. R. (2016). 4th industrial revolution and soft power. TTA Journal, 167, 4–7.
4.
Zurück zum Zitat Kim, M. S., & Choi, J. H. (2016). Understanding of the fourth industrial revolution and industrial IoT. Industrial internet. Korea Information Society Development Institute, 28(12), 20–26. Kim, M. S., & Choi, J. H. (2016). Understanding of the fourth industrial revolution and industrial IoT. Industrial internet. Korea Information Society Development Institute, 28(12), 20–26.
6.
Zurück zum Zitat Yu, Y., Jia, Z., Tao, W., Xue, B., & Lee, C. H. (2017). An efficient trust evaluation scheme for node behavior detection in the internet of things. Wireless Personal Communications, 93(2), 571–587.CrossRef Yu, Y., Jia, Z., Tao, W., Xue, B., & Lee, C. H. (2017). An efficient trust evaluation scheme for node behavior detection in the internet of things. Wireless Personal Communications, 93(2), 571–587.CrossRef
7.
Zurück zum Zitat Makoto, S. (2013). The impact of big data. Korea: HANBIT Media. Makoto, S. (2013). The impact of big data. Korea: HANBIT Media.
8.
Zurück zum Zitat Codd, E. F. (1970). A relational model of data for large shared data banks. Communications of the ACM, 13(6). Codd, E. F. (1970). A relational model of data for large shared data banks. Communications of the ACM, 13(6).
9.
Zurück zum Zitat Kim, Y. H., Kim, W. Y., & Kim, U. M. (2009). An efficient method for mining frequent patterns based on weighted support over data streams. Journal of the Korea Academia-Industrial Cooperation Society, 10(8), 1998–2004.CrossRef Kim, Y. H., Kim, W. Y., & Kim, U. M. (2009). An efficient method for mining frequent patterns based on weighted support over data streams. Journal of the Korea Academia-Industrial Cooperation Society, 10(8), 1998–2004.CrossRef
10.
Zurück zum Zitat Kholghi, M., & Keyvanpour, M. (2011). An analytical framework for data stream mining techniques based on challenges and requirements. International Journal of Engineering Science and Technology, 3(3), 2507–2513. Kholghi, M., & Keyvanpour, M. (2011). An analytical framework for data stream mining techniques based on challenges and requirements. International Journal of Engineering Science and Technology, 3(3), 2507–2513.
11.
Zurück zum Zitat Yang, J. K., Cheon, K. M., & Byun, Y. W. (2105). Manufacturing process improvement of smart phone camera body using data mining and RSM mixture model. In Proceeding of the Korea Academia-Industrial cooperation Society, (pp. 73–75). Yang, J. K., Cheon, K. M., & Byun, Y. W. (2105). Manufacturing process improvement of smart phone camera body using data mining and RSM mixture model. In Proceeding of the Korea Academia-Industrial cooperation Society, (pp. 73–75).
12.
Zurück zum Zitat Kang, E. Y. (2009). A mining-based healthcare multi-agent system in ubiquitous environments. Journal of the Korea Academia-Industrial cooperation Society, 10(9), 2354–2360.CrossRef Kang, E. Y. (2009). A mining-based healthcare multi-agent system in ubiquitous environments. Journal of the Korea Academia-Industrial cooperation Society, 10(9), 2354–2360.CrossRef
16.
Zurück zum Zitat Park, S. T., Kim, Y. R., Jeong, S. P., Hong, C. I., & Kang, T. G. (2016). A case study on effective technique of distributed data storage for big data processing in the wireless internet environment. Wireless Personal Communications, 86(1), 239–253.CrossRef Park, S. T., Kim, Y. R., Jeong, S. P., Hong, C. I., & Kang, T. G. (2016). A case study on effective technique of distributed data storage for big data processing in the wireless internet environment. Wireless Personal Communications, 86(1), 239–253.CrossRef
17.
Zurück zum Zitat Cho, Y. T., Lee, W. J., Lee, I. G., On, B. W., & Choi, J. I. (2015). Analyzing smart grid energy data using Hadoop based big data system. The Transactions of the Korean Institute of Electrical Engineers, 64P(2), 85–91.CrossRef Cho, Y. T., Lee, W. J., Lee, I. G., On, B. W., & Choi, J. I. (2015). Analyzing smart grid energy data using Hadoop based big data system. The Transactions of the Korean Institute of Electrical Engineers, 64P(2), 85–91.CrossRef
19.
Zurück zum Zitat Prajapati, V. (2013). Big data analytics with R and Hadoop. Birmingham, UK: Packt Publishing. Prajapati, V. (2013). Big data analytics with R and Hadoop. Birmingham, UK: Packt Publishing.
20.
Zurück zum Zitat Cleveland, W. S., & Guha, S. (2010). Computing environment for the statistical analysis of large and complex data, Doctoral Dissertation, Purdue University West Lafayette. Cleveland, W. S., & Guha, S. (2010). Computing environment for the statistical analysis of large and complex data, Doctoral Dissertation, Purdue University West Lafayette.
21.
Zurück zum Zitat Oancea, B., & Dragoescu, R. M. (2014). Integrating R and Hadoop for big data analysis. Revista Română de Statistică nr., 2, 83–94. Oancea, B., & Dragoescu, R. M. (2014). Integrating R and Hadoop for big data analysis. Revista Română de Statistică nr., 2, 83–94.
22.
Zurück zum Zitat Zheng, Z., Wang, P., Liu, J., & Sun, S. (2015). Real-time big data processing framework: Challenges and solutions. Applied Mathematics and Information Sciences, 9(6), 3169–3190. Zheng, Z., Wang, P., Liu, J., & Sun, S. (2015). Real-time big data processing framework: Challenges and solutions. Applied Mathematics and Information Sciences, 9(6), 3169–3190.
23.
Zurück zum Zitat Kim, S. H., & Na, W. S. (2016). Safe data transmission architecture based on cloud for internet of things. Wireless Personal Communications, 86(1), 287–300.CrossRef Kim, S. H., & Na, W. S. (2016). Safe data transmission architecture based on cloud for internet of things. Wireless Personal Communications, 86(1), 287–300.CrossRef
24.
Zurück zum Zitat Kang, Y. H. (2013). Performance analysis of MapReduce application on private cloud by using openstack. Journal of KITT, 11(12), 177–183. Kang, Y. H. (2013). Performance analysis of MapReduce application on private cloud by using openstack. Journal of KITT, 11(12), 177–183.
26.
Zurück zum Zitat Suciu, G., Vulpe, A., Martian, A., Halunga, S., & Vizireanu, D. N. (2016). Big data processing for renewable energy telemetry using a decentralized cloud M2M system. Wireless Personal Communications, 87(3), 1113–1128.CrossRef Suciu, G., Vulpe, A., Martian, A., Halunga, S., & Vizireanu, D. N. (2016). Big data processing for renewable energy telemetry using a decentralized cloud M2M system. Wireless Personal Communications, 87(3), 1113–1128.CrossRef
27.
Zurück zum Zitat Mitchell, T. M. (1997). Machine learning. Ithaca, NY: McGraw-Hill Science.MATH Mitchell, T. M. (1997). Machine learning. Ithaca, NY: McGraw-Hill Science.MATH
28.
Zurück zum Zitat Kim, E. J. (2016). Introduction to artificial intelligence, machine learning, and deep learning. Korea: Books Wiki. Kim, E. J. (2016). Introduction to artificial intelligence, machine learning, and deep learning. Korea: Books Wiki.
29.
Zurück zum Zitat Cho, S. J., & Kang, S. H. (2016). Industrial applications of machine learning (artificial intelligence). Korean Institute Industrial Engineers ie Magazine, 23(2), 34–38. Cho, S. J., & Kang, S. H. (2016). Industrial applications of machine learning (artificial intelligence). Korean Institute Industrial Engineers ie Magazine, 23(2), 34–38.
30.
Zurück zum Zitat Park, C. Y., Kim, Y. D., Kim, J. S., Song, J. W., & Choi, H. S. (2013). Data mining using R. Korea: KyoWooSa. Park, C. Y., Kim, Y. D., Kim, J. S., Song, J. W., & Choi, H. S. (2013). Data mining using R. Korea: KyoWooSa.
32.
Zurück zum Zitat Wang, Y., Chen, I. R., & Wang, D. C. (2015). A survey of mobile cloud computing applications: Perspectives and challenges. Wireless Personal Communications, 80(4), 1607–1623.MathSciNetCrossRef Wang, Y., Chen, I. R., & Wang, D. C. (2015). A survey of mobile cloud computing applications: Perspectives and challenges. Wireless Personal Communications, 80(4), 1607–1623.MathSciNetCrossRef
Metadaten
Titel
A Study on Prediction Model of Equipment Failure Through Analysis of Big Data Based on RHadoop
verfasst von
Jin-Hee Ku
Publikationsdatum
20.04.2017
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2018
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-017-4151-1

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