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Journal ArticleDOI

A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing

TLDR
A new computational framework that enables remote real-time sensing, monitoring, and scalable high performance computing for diagnosis and prognosis is introduced and a proof-of-concept prototype is developed to demonstrate how the framework can enable manufacturers to monitor machine health conditions and generate predictive analytics.
About
This article is published in Journal of Manufacturing Systems.The article was published on 2017-04-01. It has received 223 citations till now. The article focuses on the topics: Prognostics & Wireless sensor network.

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Citations
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Journal ArticleDOI

Machine vision based condition monitoring and fault diagnosis of machine tools using information from machined surface texture: A review

TL;DR: A theoretical basis and roadmap to further study or build MVCMFD-MTs using information from the machined surface texture is provided, and current challenges and potential research directions in nowadays intelligent manufacturing are discussed.
Journal ArticleDOI

Patented intelligence: Cloning human decision models for Industry 4.0

TL;DR: The Pi-Mind technology is a set of models, techniques, and tools built on principles of value-based biased decision-making and creative cognitive computing to augment the axioms of decision rationality in industry.
Journal ArticleDOI

Deep Learning for Improved System Remaining Life Prediction

TL;DR: A data-driven approach to tracking system state degradation and consequently, predicting the remaining useful life, based on the Long Short-Term Memory (LSTM) network is presented.
Journal ArticleDOI

Influence of Montoring: Fog and Edge Computing

TL;DR: Various techniques involved for monitoring for edge and fog computing and its advantages are discussed and a case study to demonstarte the need of monitoring in fog and edge in the healthcare system is concluded.
Journal ArticleDOI

Fault diagnosis using novel AdaBoost based discriminant locality preserving projection with resamples

TL;DR: Simulation results indicate that the proposed A-DLPPR model can achieve higher fault diagnosis accuracy than some other models, which verifies that in the field of complex industrial processes, the proposed AdaBoost-based discriminant locality preserving projection with resamples method can be used as an effective model for fault diagnosis.
References
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Journal ArticleDOI

Random Forests

TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
ReportDOI

The NIST Definition of Cloud Computing

Peter Mell, +1 more
TL;DR: This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.
Journal ArticleDOI

A view of cloud computing

TL;DR: The clouds are clearing the clouds away from the true potential and obstacles posed by this computing capability.
Book

The Grid 2: Blueprint for a New Computing Infrastructure

TL;DR: The Globus Toolkit as discussed by the authors is a toolkit for high-throughput resource management for distributed supercomputing applications, focusing on real-time wide-distributed instrumentation systems.
Proceedings ArticleDOI

Fog computing and its role in the internet of things

TL;DR: This paper argues that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).
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