A Sensor Model for Enhancement of Manufacturing Equipment Data Interoperability

2012 ◽  
Author(s):  
Kang B. Lee ◽  
Eugene Y. Song ◽  
Peter S. Gu
Keyword(s):  
Data Exchange ◽  
Manufacturing Systems ◽  
Sensor Data ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Sensors And Actuators ◽  
Data Interoperability ◽  
Manufacturing Equipment ◽  
Sensor Model ◽  
Smart Manufacturing Systems

Sensors can provide real-time production information to optimize manufacturing processes in a factory. Recently, more attention has been paid to the application of sensors in smart manufacturing systems. Sensor data exchange, sharing, and interoperability are challenges for manufacturing equipment monitoring in smart manufacturing. Standardized sensor data formats and communication protocols can help to solve these problems. MTConnect is an open, free, extensible protocol for the data exchange between monitoring applications and shop floor devices which include machine tools, sensors, and actuators. This paper introduces a sensor model for MTConnect to enhance manufacturing equipment data interoperability. The sensor model defines a Sensor and SensorChannel, as well as an interface to access the Sensor and its SensorChannels, which include sensing element, calibration, signal conditioning, and analog-to-digital conversion (ADC) information. The sensor model has been implemented in a virtual milling machine with a built-in sensor. Two case studies of MTConnect Probe and Sample requests for sensor information are provided to verify the sensor model.

scholarly journals Fog Computing Application of Cyber-Physical Models of IoT Devices with Symbolic Approximation Algorithms

2021 ◽  
Author(s):  
Deok-Kee Choi
Keyword(s):  
Cloud Computing ◽  
Manufacturing Systems ◽  
Fog Computing ◽  
Streaming Data ◽  
Physical Models ◽  
Sensor Data ◽  
Smart Manufacturing ◽  
Symbolic Approximation ◽  
Iot Devices ◽  
Smart Manufacturing Systems

Abstract Smart manufacturing systems transmit out streaming data from IoT devices to cloud computing; however, this could bring about several disadvantages such as high latency, immobility, and high bandwidth usage, etc. As for streaming data generated in many IoT devices, to avoid a long path from the devices to cloud computing, Fog computing has drawn in manufacturing recently much attention. This may allow IoT devices to utilize the closer resource without heavily depending on cloud computing. In this research, we set up a three-blade fan as IoT device used in manufacturing system with an accelerometer installed and analyzed the sensor data through cyber-physical models based on machine learning and streaming data analytics at Fog computing. Most of the previous studies on the similar subject are of pre-processed data open to public on the Internet, not with real-world data. Thus, studies using real-world sensor data are rarely found. A symbolic approximation algorithm is a combination of the dictionary-based algorithm of symbolic approximation algorithms and term-frequency inverse document frequency algorithm to approximate the time-series signal of sensors. We closely followed the Bayesian approach to clarify the whole procedure in a logical order. In order to monitor a fan's state in real time, we employed five different cyber-physical models, among which the symbolic approximation algorithm resulted in about 98% accuracy at a 95% confidence level with correctly classifying the current state of the fan. Furthermore, we have run statistical rigor tests on both experimental data and the simulation results through executing the post-hoc analysis. By implementing micro-intelligence with a trained cyber-physical model unto an individual IoT device through Fog computing we may alienate significant amount of load on cloud computing; thus, with saving cost on managing cloud computing facility. We would expect that this framework to be utilized for various IoT devices of smart manufacturing systems.

scholarly journals Interoperability in Smart Manufacturing: Research Challenges

Machines ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 21 ◽  
Author(s):  
Abe Zeid ◽  
Sarvesh Sundaram ◽  
Mohsen Moghaddam ◽  
Sagar Kamarthi ◽  
Tucker Marion
Keyword(s):  
Data Exchange ◽  
Research Area ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Successful Implementation ◽  
Integrated Networks ◽  
Software Packages ◽  
Industrial Internet ◽  
Logical Semantics ◽  
And Control

Recent advances in manufacturing technology, such as cyber–physical systems, industrial Internet, AI (Artificial Intelligence), and machine learning have driven the evolution of manufacturing architectures into integrated networks of automation devices, services, and enterprises. One of the resulting challenges of this evolution is the increased need for interoperability at different levels of the manufacturing ecosystem. The scope ranges from shop–floor software, devices, and control systems to Internet-based cloud-platforms, providing various services on-demand. Successful implementation of interoperability in smart manufacturing would, thus, result in effective communication and error-prone data-exchange between machines, sensors, actuators, users, systems, and platforms. A significant challenge to this is the architecture and the platforms that are used by machines and software packages. A better understanding of the subject can be achieved by studying industry-specific communication protocols and their respective logical semantics. A review of research conducted in this area is provided in this article to gain perspective on the various dimensions and types of interoperability. This article provides a multi-faceted approach to the research area of interoperability by reviewing key concepts and existing research efforts in the domain, as well as by discussing challenges and solutions.

scholarly journals A Model for Working Environment Monitoring in Smart Manufacturing

2021 ◽  
Vol 11 (6) ◽  
pp. 2850
Author(s):  
Dalibor Dobrilovic ◽  
Vladimir Brtka ◽  
Zeljko Stojanov ◽  
Gordana Jotanovic ◽  
Dragan Perakovic ◽  
...  
Keyword(s):  
Open Source ◽  
Student Teaching ◽  
Manufacturing Systems ◽  
Working Environment ◽  
Smart Manufacturing ◽  
Environment Monitoring ◽  
Application Development ◽  
Testing Platform ◽  
Proposed Model ◽  
Smart Manufacturing Systems

The growing application of smart manufacturing systems and the expansion of the Industry 4.0 model have created a need for new teaching platforms for education, rapid application development, and testing. This research addresses this need with a proposal for a model of working environment monitoring in smart manufacturing, based on emerging wireless sensor technologies and the message queuing telemetry transport (MQTT) protocol. In accordance with the proposed model, a testing platform was developed. The testing platform was built on open-source hardware and software components. The testing platform was used for the validation of the model within the presented experimental environment. The results showed that the proposed model could be developed by mainly using open-source components, which can then be used to simulate different scenarios, applications, and target systems. Furthermore, the presented stable and functional platform proved to be applicable in the process of rapid prototyping, and software development for the targeted systems, as well as for student teaching as part of the engineering education process.

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