Cloud-based machine learning for predictive analytics: Tool wear prediction in milling

Dazhong Wu, Connor Jennings, Janis Terpenny, Soundar Kumara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

The proliferation of real-time monitoring systems and the advent of Industrial Internet of Things (IIoT) over the past few years necessitates the development of scalable and parallel algorithms that help predict mechanical failures and remaining useful life of a manufacturing system or system components. Classical model-based prognostics require an in-depth physical understanding of the system of interest and oftentimes assume certain stochastic or random processes. To overcome the limitations of model-based methods, data-driven methods such as machine learning have been increasingly applied to prognostics and health management (PHM). While machine learning algorithms are able to build accurate predictive models, large volumes of training data are required. Consequently, machine learning techniques are not computationally efficient for data-driven PHM. The objective of this research is to create a novel approach for machinery prognostics using a cloud-based parallel machine learning algorithm. Specifically, one of the most popular machine learning algorithms (i.e., random forest) is applied to predict tool wear in dry milling operations. In addition, a parallel random forest algorithm is developed using the MapReduce framework and then implemented on the Amazon Elastic Compute Cloud. Experimental results have shown that the random forest algorithm can generate very accurate predictions. Moreover, significant speedup can be achieved by implementing the parallel random forest algorithm.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
EditorsRonay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2062-2069
Number of pages8
ISBN (Electronic)9781467390040
DOIs
StatePublished - Jan 1 2016
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: Dec 5 2016Dec 8 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

Other

Other4th IEEE International Conference on Big Data, Big Data 2016
CountryUnited States
CityWashington
Period12/5/1612/8/16

Fingerprint

Learning systems
Wear of materials
Learning algorithms
Random processes
Health
Parallel algorithms
Machinery
Predictive analytics
Monitoring

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Hardware and Architecture

Cite this

Wu, D., Jennings, C., Terpenny, J., & Kumara, S. (2016). Cloud-based machine learning for predictive analytics: Tool wear prediction in milling. In R. Ak, G. Karypis, Y. Xia, X. T. Hu, P. S. Yu, J. Joshi, L. Ungar, L. Liu, A-H. Sato, T. Suzumura, S. Rachuri, R. Govindaraju, ... W. Xu (Eds.), Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016 (pp. 2062-2069). [7840831] (Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2016.7840831
Wu, Dazhong ; Jennings, Connor ; Terpenny, Janis ; Kumara, Soundar. / Cloud-based machine learning for predictive analytics : Tool wear prediction in milling. Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016. editor / Ronay Ak ; George Karypis ; Yinglong Xia ; Xiaohua Tony Hu ; Philip S. Yu ; James Joshi ; Lyle Ungar ; Ling Liu ; Aki-Hiro Sato ; Toyotaro Suzumura ; Sudarsan Rachuri ; Rama Govindaraju ; Weijia Xu. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 2062-2069 (Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016).
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Wu, D, Jennings, C, Terpenny, J & Kumara, S 2016, Cloud-based machine learning for predictive analytics: Tool wear prediction in milling. in R Ak, G Karypis, Y Xia, XT Hu, PS Yu, J Joshi, L Ungar, L Liu, A-H Sato, T Suzumura, S Rachuri, R Govindaraju & W Xu (eds), Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016., 7840831, Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016, Institute of Electrical and Electronics Engineers Inc., pp. 2062-2069, 4th IEEE International Conference on Big Data, Big Data 2016, Washington, United States, 12/5/16. https://doi.org/10.1109/BigData.2016.7840831

Cloud-based machine learning for predictive analytics : Tool wear prediction in milling. / Wu, Dazhong; Jennings, Connor; Terpenny, Janis; Kumara, Soundar.

Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016. ed. / Ronay Ak; George Karypis; Yinglong Xia; Xiaohua Tony Hu; Philip S. Yu; James Joshi; Lyle Ungar; Ling Liu; Aki-Hiro Sato; Toyotaro Suzumura; Sudarsan Rachuri; Rama Govindaraju; Weijia Xu. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2062-2069 7840831 (Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Cloud-based machine learning for predictive analytics

T2 - Tool wear prediction in milling

AU - Wu, Dazhong

AU - Jennings, Connor

AU - Terpenny, Janis

AU - Kumara, Soundar

PY - 2016/1/1

Y1 - 2016/1/1

N2 - The proliferation of real-time monitoring systems and the advent of Industrial Internet of Things (IIoT) over the past few years necessitates the development of scalable and parallel algorithms that help predict mechanical failures and remaining useful life of a manufacturing system or system components. Classical model-based prognostics require an in-depth physical understanding of the system of interest and oftentimes assume certain stochastic or random processes. To overcome the limitations of model-based methods, data-driven methods such as machine learning have been increasingly applied to prognostics and health management (PHM). While machine learning algorithms are able to build accurate predictive models, large volumes of training data are required. Consequently, machine learning techniques are not computationally efficient for data-driven PHM. The objective of this research is to create a novel approach for machinery prognostics using a cloud-based parallel machine learning algorithm. Specifically, one of the most popular machine learning algorithms (i.e., random forest) is applied to predict tool wear in dry milling operations. In addition, a parallel random forest algorithm is developed using the MapReduce framework and then implemented on the Amazon Elastic Compute Cloud. Experimental results have shown that the random forest algorithm can generate very accurate predictions. Moreover, significant speedup can be achieved by implementing the parallel random forest algorithm.

AB - The proliferation of real-time monitoring systems and the advent of Industrial Internet of Things (IIoT) over the past few years necessitates the development of scalable and parallel algorithms that help predict mechanical failures and remaining useful life of a manufacturing system or system components. Classical model-based prognostics require an in-depth physical understanding of the system of interest and oftentimes assume certain stochastic or random processes. To overcome the limitations of model-based methods, data-driven methods such as machine learning have been increasingly applied to prognostics and health management (PHM). While machine learning algorithms are able to build accurate predictive models, large volumes of training data are required. Consequently, machine learning techniques are not computationally efficient for data-driven PHM. The objective of this research is to create a novel approach for machinery prognostics using a cloud-based parallel machine learning algorithm. Specifically, one of the most popular machine learning algorithms (i.e., random forest) is applied to predict tool wear in dry milling operations. In addition, a parallel random forest algorithm is developed using the MapReduce framework and then implemented on the Amazon Elastic Compute Cloud. Experimental results have shown that the random forest algorithm can generate very accurate predictions. Moreover, significant speedup can be achieved by implementing the parallel random forest algorithm.

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M3 - Conference contribution

AN - SCOPUS:85015153159

T3 - Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

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BT - Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

A2 - Ak, Ronay

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A2 - Joshi, James

A2 - Ungar, Lyle

A2 - Liu, Ling

A2 - Sato, Aki-Hiro

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PB - Institute of Electrical and Electronics Engineers Inc.

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Wu D, Jennings C, Terpenny J, Kumara S. Cloud-based machine learning for predictive analytics: Tool wear prediction in milling. In Ak R, Karypis G, Xia Y, Hu XT, Yu PS, Joshi J, Ungar L, Liu L, Sato A-H, Suzumura T, Rachuri S, Govindaraju R, Xu W, editors, Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 2062-2069. 7840831. (Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016). https://doi.org/10.1109/BigData.2016.7840831