A comparison of fog and cloud computing cyber-physical interfaces for Industry 4.0 real-time embedded machine learning engineering applications

2019 ◽  
Vol 110 ◽  
pp. 12-35 ◽  
Author(s):  
Peter O’Donovan ◽  
Colm Gallagher ◽  
Kevin Leahy ◽  
Dominic T.J. O’Sullivan
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Real Time ◽  
Industry 4.0 ◽  
Engineering Applications ◽  
Physical Interfaces

scholarly journals A Dynamic Route-Planning System Based on Industry 4.0 Technology

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 308
Author(s):  
Duy Nguyen Duc ◽  
Thong Tran Huu ◽  
Narameth Nananukul
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Industry 4.0 ◽  
Big Data Analytics ◽  
Route Planning ◽  
Research Area ◽  
Automated Guided Vehicles ◽  
Planning System ◽  
Routing Problem ◽  
Dynamic Route

Due to the availability of Industry 4.0 technology, the application of big data analytics to automated systems is possible. The distribution of products between warehouses or within a warehouse is an area that can benefit from automation based on Industry 4.0 technology. In this paper, the focus was on developing a dynamic route-planning system for automated guided vehicles within a warehouse. A dynamic routing problem with real-time obstacles was considered in this research. A key problem in this research area is the lack of a real-time route-planning algorithm that is suitable for the implementation on automated guided vehicles with limited computing resources. An optimization model, as well as machine learning methodologies for determining an operational route for the problem, is proposed. An internal layout of the warehouse of a large consumer product distributor was used to test the performance of the methodologies. A simulation environment based on Gazebo was developed and used for testing the implementation of the route-planning system. Computational results show that the proposed machine learning methodologies were able to generate routes with testing accuracy of up to 98% for a practical internal layout of a warehouse with 18 storage racks and 67 path segments. Managerial insights into how the machine learning configuration affects the prediction accuracy are also provided.

scholarly journals Real-time Machine Health Monitoring System using Machine Learning with IoT Technology

2021 ◽  
Vol 335 ◽  
pp. 02005
Author(s):  
Tzen Ket Wong ◽  
Hou Kit Mun ◽  
Swee King Phang ◽  
Kai Lok Lum ◽  
Wei Qiang Tan
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring System ◽  
Health Monitoring ◽  
Industry 4.0 ◽  
Phase System ◽  
Time Machine ◽  
Health Monitoring System ◽  
Machine Health Monitoring ◽  
Machine Health

Machine health monitoring is the main focal point for now as many industries are evolving to industry 4.0. Industry 4.0 is the revolution in industrial that involve the Internet of Things (IoT) and artificial intelligence toward automation and data sharing for production efficiency improvement. The existing established methods for machine health monitoring were not in real-time and there was no real-time correction of data from the load and processing of data on the computer. In tracking machine health efficiency this approach wasn’t very successful. Real-time machine health monitoring can improve overall equipment effectiveness (OEE), reduce electricity consumption, minimize unplanned downtime, and extend machine lifetime. In this research paper, we propose to design a real-time machine health monitoring system using machine learning with IoT technology that can analyze the supply balancing condition on a 3-phase system. This system is built with compact physical hardware and can capture the electrical data from the load then send it to the server. The server will progress data and train the data using machine learning. The system was installed on a blender machine in a factory. In this research, a system which is able to monitor the machine operation and classify the operation stages of the machine was developed. Besides that, the system also capable to monitor the load balancing condition of the machine.

scholarly journals IDS PROTOTYPE FOR INTRUSION DETECTION WITH MACHINE LEARNING MODELS IN IOT SYSTEMS OF THE INDUSTRY 4.0

10.6036/10011 ◽  
2021 ◽  
Vol 96 (3) ◽  
pp. 270-275
Author(s):  
JOSE AVELEIRA MATA ◽  
ANGEL LUIS MUÑOZ CASTAÑEDA ◽  
MARÍA TERESA GARCÍA ORDÁS ◽  
CARMEN BENAVIDES CUELLAR ◽  
José Alberto Benítez Andrades ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Industry 4.0 ◽  
Detection System ◽  
Remote Access ◽  
Security Requirements ◽  
Cloud Processing ◽  
Machine Learning Model ◽  
Great Growth

Industry 4.0 significantly improves productivity by collecting and analyzing data in real time. This, combined with remote access functions, and cloud processing that allows Internet of Things IoT, provides information that optimizes processes and decision support. Also involves a great growth of new networks and systems with special features, which mean that they are vulnerable to different attacks. So new security requirements are emerging in the IoT network. To improve the security of an IoT system for a transparent way, it is proposed the development of a prototype intrusion detection system IDS, which detects anomalies in IoT environments using the MQTT protocol (Message Queuing Telemetry Transport), widely used in IoT systems. For this purpose, it is generated a dataset of an IoT system in which perform different attacks on the MQTT protocol. This dataset is used to train a machine learning model, which is implemented in the IDS that captures the network frames in real time from the system to classify and detect the different attacks. Keywords: IoT, industry 4.0, cybersecurity, IDS, MQTT protocol, Machine Learning.

Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning

TAPPI Journal ◽  
2019 ◽  
Vol 18 (11) ◽  
pp. 679-689
Author(s):  
CYDNEY RECHTIN ◽  
CHITTA RANJAN ◽  
ANTHONY LEWIS ◽  
BETH ANN ZARKO
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Analytics ◽  
Paper Industry ◽  
Quality Parameters ◽  
Fast Change ◽  
The Face ◽  
Model Tuning ◽  
Reducing Costs ◽  
Time Critical

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

Industry 4.0: The Importance Factors of Innovation in Adopting Cloud Computing Among SMES in Malaysia

2020 ◽  
Vol 11 (1) ◽  
pp. 84-84
Author(s):  
Siti Salwa Sheikh Mokhtar ◽  
Anuar Shah Bali Mahomed ◽  
Yuhanis Abdul Aziz ◽  
Suhaimi Ab. Rahman
Keyword(s):  
Cloud Computing ◽  
Technology Acceptance ◽  
Industry 4.0 ◽  
Structural Equation ◽  
Industrial Revolution ◽  
Equation Model ◽  
Computing Industry ◽  
Global Economic Growth ◽  
Acceptance Model ◽  
Third Industrial Revolution

Small and medium-sized enterprises (SMEs) are commonly perceived as an essential part of boosting and stabilizing global economic growth. In 2018, SMEs recorded a 38.3% contribution to GDP of RM521.7 billion compared to RM491.2 billion in 2017. SMEs are expected to contribute 50% to Malaysia's GDP by 2030 relative to its present 38% contribution. However, in the context of Malaysia businesses, particularly small and medium-sized enterprises, are still not embracing the latest technology revolution sufficiently, as reported by the Ministry of International Trade and Industry (2018). Malaysia is currently in the third industrial revolution (automation), and some are still under the second industrial revolution. Such matter is worrying as only a few industries in Malaysia can adopt pillars of Industry 4.0, where business owners in Malaysia were still hesitant to embrace technologies such as the cloud. To bridge the gap in this analysis, this research adopted the technology acceptance model developed by Davis (1989) and Rogers' Diffusion Innovation Theory (1995), which incorporates the contexts of technology and innovation among SMEs in Malaysia. By using survey questionnaires, data was collected among manufacturing and services SMEs in Malaysia. Structural equation model employed to assess the important factors of innovation in adopting cloud computing among SMEs in Malaysia by using Smart-PLS. Keywords: Cloud computing, Industry 4.0, Innovation, Technological

Speech Organ Contour Extraction Using Real-Time MRI and Machine Learning Method

2019 ◽  
Author(s):  
Hironori Takemoto ◽  
Tsubasa Goto ◽  
Yuya Hagihara ◽  
Sayaka Hamanaka ◽  
Tatsuya Kitamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Contour Extraction
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