A Computational Framework for Cloud-based Machine Prognosis

Peng Wang, Robert X. Gao, Dazhong Wu, Janis Terpenny

Research output: Contribution to journalConference article

6 Citations (Scopus)

Abstract

Prognosis of machine degradation and failure propagation is essential to preventative maintenance scheduling and sustainable manufacturing. Emerging technologies such as Internet of Things (IoT) and cloud computing offer new opportunities for scaling up computing performance and capacity for machine monitoring and prognosis. This paper addresses challenges in machine prognosis due to high-speed data streaming from real-time sensing by leveraging parallel computing on the cloud. A framework for cloud-based prognosis is then presented to model the relationships between hidden machine states and sensor measurements under varying operating conditions and maintenance actions. To account for uncertainties associated with model representation and/or measurement quality, each relationship is modeled as a probability distribution and estimated through either model-based (e.g. particle filtering) or data-driven algorithms (e.g. support vector machine), according to the available physical/mathematical description of the relationship. A complete prognostic model of the machine is then constructed by merging the individual probability distributions. The computational process is implemented on the MapReduce-based cloud computing platform. Prognosis of the entire machine is accomplished by aggregating prognosis results of the individual components, through a separate parallel computing process. The proposed framework is experimentally demonstrated using tool data collected from CNC machines.

Original languageEnglish (US)
Pages (from-to)309-314
Number of pages6
JournalProcedia CIRP
Volume57
DOIs
StatePublished - Jan 1 2016
Event49th CIRP Conference on Manufacturing Systems, CIRP-CMS 2016 - Stuttgart, Germany
Duration: May 25 2016May 27 2016

Fingerprint

Parallel processing systems
Cloud computing
Probability distributions
Merging
Support vector machines
Scheduling
Degradation
Monitoring
Sensors
Internet of things
Uncertainty

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Cite this

Wang, Peng ; Gao, Robert X. ; Wu, Dazhong ; Terpenny, Janis. / A Computational Framework for Cloud-based Machine Prognosis. In: Procedia CIRP. 2016 ; Vol. 57. pp. 309-314.
@article{8c1d0747034b4b5dbefde2461c8f8412,
title = "A Computational Framework for Cloud-based Machine Prognosis",
abstract = "Prognosis of machine degradation and failure propagation is essential to preventative maintenance scheduling and sustainable manufacturing. Emerging technologies such as Internet of Things (IoT) and cloud computing offer new opportunities for scaling up computing performance and capacity for machine monitoring and prognosis. This paper addresses challenges in machine prognosis due to high-speed data streaming from real-time sensing by leveraging parallel computing on the cloud. A framework for cloud-based prognosis is then presented to model the relationships between hidden machine states and sensor measurements under varying operating conditions and maintenance actions. To account for uncertainties associated with model representation and/or measurement quality, each relationship is modeled as a probability distribution and estimated through either model-based (e.g. particle filtering) or data-driven algorithms (e.g. support vector machine), according to the available physical/mathematical description of the relationship. A complete prognostic model of the machine is then constructed by merging the individual probability distributions. The computational process is implemented on the MapReduce-based cloud computing platform. Prognosis of the entire machine is accomplished by aggregating prognosis results of the individual components, through a separate parallel computing process. The proposed framework is experimentally demonstrated using tool data collected from CNC machines.",
author = "Peng Wang and Gao, {Robert X.} and Dazhong Wu and Janis Terpenny",
year = "2016",
month = "1",
day = "1",
doi = "10.1016/j.procir.2016.11.054",
language = "English (US)",
volume = "57",
pages = "309--314",
journal = "Procedia CIRP",
issn = "2212-8271",
publisher = "Elsevier BV",

}

A Computational Framework for Cloud-based Machine Prognosis. / Wang, Peng; Gao, Robert X.; Wu, Dazhong; Terpenny, Janis.

In: Procedia CIRP, Vol. 57, 01.01.2016, p. 309-314.

Research output: Contribution to journalConference article

TY - JOUR

T1 - A Computational Framework for Cloud-based Machine Prognosis

AU - Wang, Peng

AU - Gao, Robert X.

AU - Wu, Dazhong

AU - Terpenny, Janis

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Prognosis of machine degradation and failure propagation is essential to preventative maintenance scheduling and sustainable manufacturing. Emerging technologies such as Internet of Things (IoT) and cloud computing offer new opportunities for scaling up computing performance and capacity for machine monitoring and prognosis. This paper addresses challenges in machine prognosis due to high-speed data streaming from real-time sensing by leveraging parallel computing on the cloud. A framework for cloud-based prognosis is then presented to model the relationships between hidden machine states and sensor measurements under varying operating conditions and maintenance actions. To account for uncertainties associated with model representation and/or measurement quality, each relationship is modeled as a probability distribution and estimated through either model-based (e.g. particle filtering) or data-driven algorithms (e.g. support vector machine), according to the available physical/mathematical description of the relationship. A complete prognostic model of the machine is then constructed by merging the individual probability distributions. The computational process is implemented on the MapReduce-based cloud computing platform. Prognosis of the entire machine is accomplished by aggregating prognosis results of the individual components, through a separate parallel computing process. The proposed framework is experimentally demonstrated using tool data collected from CNC machines.

AB - Prognosis of machine degradation and failure propagation is essential to preventative maintenance scheduling and sustainable manufacturing. Emerging technologies such as Internet of Things (IoT) and cloud computing offer new opportunities for scaling up computing performance and capacity for machine monitoring and prognosis. This paper addresses challenges in machine prognosis due to high-speed data streaming from real-time sensing by leveraging parallel computing on the cloud. A framework for cloud-based prognosis is then presented to model the relationships between hidden machine states and sensor measurements under varying operating conditions and maintenance actions. To account for uncertainties associated with model representation and/or measurement quality, each relationship is modeled as a probability distribution and estimated through either model-based (e.g. particle filtering) or data-driven algorithms (e.g. support vector machine), according to the available physical/mathematical description of the relationship. A complete prognostic model of the machine is then constructed by merging the individual probability distributions. The computational process is implemented on the MapReduce-based cloud computing platform. Prognosis of the entire machine is accomplished by aggregating prognosis results of the individual components, through a separate parallel computing process. The proposed framework is experimentally demonstrated using tool data collected from CNC machines.

UR - http://www.scopus.com/inward/record.url?scp=85006979721&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85006979721&partnerID=8YFLogxK

U2 - 10.1016/j.procir.2016.11.054

DO - 10.1016/j.procir.2016.11.054

M3 - Conference article

AN - SCOPUS:85006979721

VL - 57

SP - 309

EP - 314

JO - Procedia CIRP

JF - Procedia CIRP

SN - 2212-8271

ER -