| 项目编号: | 1547124
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| 项目名称: | EAGER: Cybermanufacturing:Collaborative Research: A novel process data analytics framework for IoT-enabled cybermanufacturing |
| 作者: | Nader Vahdat
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| 承担单位: | Tuskegee University
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| 批准年: | 2014
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| 开始日期: | 2015-09-15
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| 结束日期: | 2018-08-31
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| 资助金额: | USD59643
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Engineering - Chemical, Bioengineering, Environmental, and Transport Systems
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| 英文关键词: | datum
; cybermanufacturing system monitoring
; datum analytic
; year
; information-based
; statistics pattern
; iot device
; framework
; information
; modeling framework
; unimagined potential use
; bayesian-based event classification
; industrial internet
; key capability
; self-adaptive modeling
; new embedded computing capability
; networked information-based technology
; plant downtime
; local redundancy
; process nonlinearity
; abstract wang/he
; advanced manufacturing partnership
; iot-enabled manufacturing technology testbed
; big data
; collaborative proposal
; order statistics
; next generation
; predictive maintenance
; process monitoring
; low-cost computation
; potential solution
; process variable
; 3rd generation statistical process monitoring method
; eager proposal
; potential fault
; non-connected counterpart
; process/product design
; huge amount
; general consensus
; smart diagnostic
; new generation
; massive number
; information-based technology
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| 英文摘要: | Abstract Wang/He, 1547163/1547124 (Collaborative proposal)
There is general consensus that factories and plants that are connected to the internet are more efficient, productive and smarter than their non-connected counterparts. Next generation manufacturing systems are expected to include the application of increasingly powerful and low-cost computation and networked information-based technologies. IoT devices are sensors/actuators, computers with wireless networks that are small and easy to embed. IoT devices offer the opportunity to instrument systems with massive numbers of sensors. With the huge amount of data and the programmability of IoT devices, comes the opportunity to shape the data received, to address local redundancy of information, and to improve both the accuracy and precision of measurements locally and across a distributed parameter system such as a reactor.
With the emergence of the Industrial Internet of Things (IoT) and ever advancing computing power and expansion of wireless networking technologies, a new generation of networked, information-based technologies, data analytics, and predictive modeling are providing new embedded computing capabilities as well as access to previously unimagined potential uses of data and information. These capabilities provide possibilities for new, radically better ways of doing manufacturing. As noted in Advanced Manufacturing Partnership 2.0 (AMP 2.0), if potential faults and failures are detected and corrected while still incipient, reduction of plant downtimes of 50% in five years and 90% in ten years may be achieved. Converting these possibilities into reality remains challenging. In this EAGER proposal, the PIs propose a new process data analytics framework with the aim of providing smart diagnostics and prognostics for cybermanufacturing. As part of this effort, they also propose to establish an IoT-enabled manufacturing technology testbed (MTT) to explore and establish a proof-of-concept for the proposed framework.
The PIs propose a statistics pattern based process monitoring (SPPM) framework as one of the potential solutions. This SPPM will make use of the higher order statistics of process variables that have not been utilized before to directly quantify the process nonlinearity and nonnormality. In addition, a Bayesian-based event classification is proposed to enable intelligent, self-adaptive modeling, a key capability of cybermanufacturing system monitoring. By establishing an IoT enabled manufacturing technology testbed and running designed experiments, this project should yield a better understanding of the properties, capacities and performances of IoT devices. If successful, this project will create one of the first prototypes of the 3rd generation statistical process monitoring methods. The idea of the proposed statistics pattern based data analytics is not limited to process monitoring. It provides a modeling framework that can be applied for process/product design and predictive maintenance by relating cybermanufacturing Big Data to different objectives. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/93213
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| Appears in Collections: | 影响、适应和脆弱性
气候减缓与适应
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| Recommended Citation: | |
Nader Vahdat. EAGER: Cybermanufacturing:Collaborative Research: A novel process data analytics framework for IoT-enabled cybermanufacturing. 2014-01-01.
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