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Special Section Articles

A Unified Framework and Platform for Designing of Cloud-Based Machine Health Monitoring and Manufacturing Systems

[+] Author and Article Information
Shanhu Yang

NSF I/UCRC for Intelligent Maintenance Systems (IMS),
Center for Intelligent Maintenance Systems,
University of Cincinnati,
560 Baldwin Hall,
PO Box 210072,
Cincinnati, OH 45221
e-mail: shanhuyang@gmail.com

Behrad Bagheri

NSF I/UCRC for Intelligent Maintenance Systems (IMS),
Center for Intelligent Maintenance Systems,
University of Cincinnati,
560 Baldwin Hall,
PO Box 210072,
Cincinnati, OH 45221
e-mail: bagherbd@mail.uc.edu

Hung-An Kao

NSF I/UCRC for Intelligent Maintenance Systems (IMS),
Center for Intelligent Maintenance Systems,
University of Cincinnati,
560 Baldwin Hall,
PO Box 210072,
Cincinnati, OH 45221
e-mail: kaohn@mail.uc.edu

Jay Lee

NSF I/UCRC for Intelligent Maintenance Systems (IMS),
Center for Intelligent Maintenance Systems,
University of Cincinnati,
560 Baldwin Hall,
PO Box 210072,
Cincinnati, OH 45221
e-mail: jay.lee@uc.edu

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received October 13, 2014; final manuscript received May 9, 2015; published online July 8, 2015. Assoc. Editor: Lihui Wang.

J. Manuf. Sci. Eng 137(4), 040914 (Aug 01, 2015) (6 pages) Paper No: MANU-14-1524; doi: 10.1115/1.4030669 History: Received October 13, 2014; Revised May 09, 2015; Online July 08, 2015

Cloud computing has brought about new service models and research opportunities in the manufacturing and service industries with advantages in ubiquitous accessibility, convenient scalability, and mobility. With the emerging industrial big data prompted by the advent of the internet of things and the wide implementation of sensor networks, the cloud computing paradigm can be utilized as a hosting platform for autonomous data mining and cognitive learning algorithms. For machine health monitoring and prognostics, we investigate the challenges imposed by industrial big data such as heterogeneous data format and complex machine working conditions and further propose a systematically designed framework as a guideline for implementing cloud-based machine health prognostics. Specifically, to ensure the effectiveness and adaptability of the cloud platform for machines under complex working conditions, two key design methodologies are presented which include the standardized feature extraction scheme and an adaptive prognostics algorithm. The proposed strategy is further demonstrated using a case study of machining processes.

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Figures

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Fig. 1

Overall scope for cloud-based machine health prognostics

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Fig. 2

Data organization and standardized feature extraction procedure

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Fig. 3

Features extracted from three data types

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Fig. 4

Adaptive analytical engine for cloud-based PHM

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Fig. 5

Relationship among system performance, system health, and working regimes

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Fig. 6

Adaptive health state recognition for blade condition

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Fig. 7

The design of user interface for the platform

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