EECS MSc student, Yyi Kai Teoh, has published his MSc dissertation in IEEE Internet of Things Journal (Q1 Journal with IF: 9.936).
Yyi Kai’s dissertation investigated into IoT and Fog Computing based Predictive Maintenance Model for Effective Asset Management in Industry 4.0 using Machine Learning.
"Having my dissertation published in a reputable and respectful publisher, IEEE Internet of Things Journal is a sense of accomplishment and a thirst for more. IEEE IoT Journal publishes papers on the latest advances on the various aspects of IoT.
It was one my greatest moment when I received an email from the journal editor telling that “your paper has been accepted for publication”. This was my first academic work published in the academic journal article. I believe that breaking the first barrier boosts my confidence to deliver more quality academic and industry research.
I would like to address my appreciation towards my supervisor, Dr Sukhpal Singh Gill, and co-author, Dr Ajith Kumar Parlikad (Reader, Cambridge University) for their generous efforts guiding and support throughout the research period."
The assets in Industry 4.0 are categorised into physical, virtual and human. The innovation and popularisation of ubiquitous computing enhance the usage of smart devices: RFID tags, QR codes, LoRa tags, etc. for assets identification and
tracking. The generated data from Industrial Internet of Things (IIoT) eases information visibility and process automation in Industry 4.0. Virtual assets include the data produced from IIoT. One of the applications of the industrial big data is to predict the failure of manufacturing equipment. Predictive maintenance enables the business owner to decide such as repairing or replacing the component before an actual failure which affects the whole production line. Therefore, Industry 4.0 requires an effective asset management to optimise the tasks distributions and predictive maintenance model. This paper presents the Genetic Algorithm (GA) based resource management integrating with machine learning for predictive maintenance in fog computing. The time, cost and energy performance of GA along with MinMin, MaxMin, FCFS, RoundRobin are simulated in the FogWorkflowsim. The predictive maintenance model is built in two-class logistic regression using real-time datasets. The results demonstrate that the proposed technique outperforms MinMin, MaxMin, FCFS, RoundRobin in execution time, cost and energy usage. The execution time is 0.48% faster, 5.43% lower cost and energy usage is 28.10% lower in comparison with second-best results. The training and testing accuracy of the prediction model is 95.1% and 94.5%, respectively.
Y. K. Teoh, S. S. Gill and A. K. Parlikad, "IoT and Fog Computing based Predictive Maintenance Model for Effective Asset Management in Industry 4.0 using Machine Learning," in IEEE Internet of Things Journal, 2021. doi: 10.1109/JIOT.2021.3050441.
Read the full publication here: https://ieeexplore.ieee.org/document/9319212