Research on IoT based Cyber Physical System for Industrial big data Analytics

Lee, C. K. M.; Yeung, Chi Ho; Cheng, Mei Na

doi:10.1109/ieem.2015.7385969

Cited by 34 publications

(9 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such framework is capable to collect the data from the sensors installed in the buildings and performs data analytics for decision making. Moreover, industrial production can be improved using an IoT based cyber physical system that is equipped with an information analysis and knowledge acquisition techniques [60]. Traffic congestion is an important issue with smart cities.…”

Section: Importance Of Big Data Analytics In Iotmentioning

confidence: 99%

Internet of Things is a revolutionary approach for future technology enhancement: a review

2019

View full text Add to dashboard Cite

Internet of Things (IoT) is a new paradigm that has changed the traditional way of living into a high tech life style. Smart city, smart homes, pollution control, energy saving, smart transportation, smart industries are such transformations due to IoT. A lot of crucial research studies and investigations have been done in order to enhance the technology through IoT. However, there are still a lot of challenges and issues that need to be addressed to achieve the full potential of IoT. These challenges and issues must be considered from various aspects of IoT such as applications, challenges, enabling technologies, social and environmental impacts etc. The main goal of this review article is to provide a detailed discussion from both technological and social perspective. The article discusses different challenges and key issues of IoT, architecture and important application domains. Also, the article bring into light the existing literature and illustrated their contribution in different aspects of IoT. Moreover, the importance of big data and its analysis with respect to IoT has been discussed. This article would help the readers and researcher to understand the IoT and its applicability to the real world.

show abstract

Section: Importance Of Big Data Analytics In Iotmentioning

confidence: 99%

Internet of Things is a revolutionary approach for future technology enhancement: a review

2019

View full text Add to dashboard Cite

show abstract

“…In particular, we observed a gap in the application of both prediction models and maintenance optimization (Wildeman et al, 1997;Dekker et al, 1997;Van Horenbeek et al, 2010;Lee,et al, 2015a;Zheng et al, 2017;Al-Dulaimi et al, 2019;Sang et al, 2020), in the context of Industry 4.0, particularly for supporting scheduling of multiple machine components. Thus, PMS4MMC is proposed in Predictive Maintenance Schedule for Multiple Machines and Components.…”

Section: Discussionmentioning

confidence: 99%

“…Handling the demands, i.e., highly collaborative complex systems and multiple machines of Industry 4.0, focusing on manufacturing is challenging (Thoben et al, 2017). From the requirements of Industry 4.0 in the literature studies (Lee et al, 2015a;Thoben et al, 2017;Sang et al, 2020aSang et al, , 2020bSang et al, 2020;Zonta et al, 2020;Sang et al, 2021), several key factors need to be considered for an optimal maintenance schedule plan of PMS4MMC. This includes data-driven maintenance, i.e., predictive models such as RUL, multiple machine components, maintenance tasks, maintenance time, cost, and the resource aspect, i.e., availability status of each component and engineer.…”

Section: Approach For Industry 40 Maintenance Optimizationmentioning

confidence: 99%

A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0

Sang

Vrieze

2021

Front. Big Data

View full text Add to dashboard Cite

The Industry 4.0 paradigm is the focus of modern manufacturing system design. The integration of cutting-edge technologies such as the Internet of things, cyber–physical systems, big data analytics, and cloud computing requires a flexible platform supporting the effective optimization of manufacturing-related processes, e.g., predictive maintenance. Existing predictive maintenance studies generally focus on either a predictive model without considering the maintenance decisions or maintenance optimizations based on the degradation models of the known system. To address this, we propose PMMI 4.0, a Predictive Maintenance Model for Industry 4.0, which utilizes a newly proposed solution PMS4MMC for supporting an optimized maintenance schedule plan for multiple machine components driven by a data-driven LSTM model for RUL (remaining useful life) estimation. The effectiveness of the proposed solution is demonstrated using a real-world industrial case with related data. The results showed the validity and applicability of this work.

show abstract

“…✓ (CLEVER) ✓ ✗ ✗ ✗ Smart environ. Lee, Yeung, and Cheng (2015) ✓ (generic) ✗ ✓ ✓ ✗ Smart environ. Tai et al (2015) ✓ ( Such applications to a wider range of phenomena and the according sensor networks would already allow us to tap into a large part of senses about our environment.…”

Section: Discussionmentioning

confidence: 99%

A domain-independent methodology to analyze IoT data streams in real-time. A proof of concept implementation for anomaly detection from environmental data

Trilles

Belmonte

Schade

et al. 2016

International Journal of Digital Earth

View full text Add to dashboard Cite

Pushed by the Internet of Things (IoT) paradigm modern sensor networks monitor a wide range of phenomena, in areas such as environmental monitoring, health care, industrial processes, and smart cities. These networks provide a continuous pulse of the almost infinite activities that are happening in the physical space and are thus, key enablers for a Digital Earth Nervous System. Nevertheless, the rapid processing of these sensor data streams still continues to challenge traditional data-handling solutions and new approaches are being requested. We propose a generic answer to this challenge, which has the potential to support any form of distributed real-time analysis. This neutral methodology follows a brokering approach to work with different kinds of data sources and uses web-based standards to achieve interoperability. As a proof of concept, we implemented the methodology to detect anomalies in real-time and applied it to the area of environmental monitoring. The developed system is capable of detecting anomalies, generating notifications, and displaying the recent situation to the user. ARTICLE HISTORY

show abstract

Research on IoT based Cyber Physical System for Industrial big data Analytics

Cited by 34 publications

References 6 publications

Internet of Things is a revolutionary approach for future technology enhancement: a review

Internet of Things is a revolutionary approach for future technology enhancement: a review

A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0

A domain-independent methodology to analyze IoT data streams in real-time. A proof of concept implementation for anomaly detection from environmental data

Contact Info

Product

Resources

About