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Dr Hang Ruan

My publications


Dorneanu Bogdan, Ruan Hang, Mohamed Abdelrahim, Heshmat Mohamed, Gao Yang, Xiao Pei, Arellano-Garcia Harvey (2019) Towards fault detection and self-healing of chemical processes over wireless sensor networks, Industry 4.0 ? Shaping the Future of the Digital World Taylor & Francis
This contribution introduces a framework for the fault detection and healing of chemical processes over wireless sensor networks. The approach considers the development of a hybrid system which consists of a fault detection method based on machine learning, a wireless communication model and an ontology-based multi-agent system with a cooperative control for the process monitoring.
Dorneau Bogdan, Heshmat Mohamed, Mohamed Abdelrahim, Ruan Hang, Gao Yang, Xiao Pei, Arellano-Garcia Harvey (2020) Stepping towards the industrial Sixth Sense, ESCAPE30
This contribution introduces the development of an intelligent monitoring and control framework for chemical processes, integrating the advantages of Industry 4.0 technologies, cooperative control and fault detection via wireless sensor networks. Using information on the process? structure and behaviour, equipment information, and expert knowledge, the system is able to detect faults. The integration with the monitoring system facilitates the detection and optimises the controller?s actions. The results indicate that the proposed approach achieves high fault detection accuracy based on plant measurements, while the cooperative controllers improve the control of the process.
Mohamed Abdelrahim, Ruan Hang, Abdelwahab Mohamed, Dorneanu Bogdan, Xiao Pei, Arellano-Garcia Harvey, Gao Yang, Tafazolli Rahim An Inter-disciplinary Modelling Approach in
Industrial 5G/6G and Machine Learning Era,
IEEE International Conference on Communications (ICC) IEEE
Recently, the fifth-generation (5G) cellular system
has been standardised. As opposed to legacy cellular systems geared towards broadband services, the 5G system identifies key use cases for ultra-reliable and low latency communications
(URLLC) and massive machine-type communications (mMTC).
These intrinsic 5G capabilities enable promising sensor-based vertical applications and services such as industrial process automation. The latter includes autonomous fault detection and prediction, optimised operations and proactive control.
Such applications enable equipping industrial plants with a sixth sense (6S) for optimised operations and fault avoidance. In this direction, we introduce an inter-disciplinary approach integrating wireless sensor networks with machine learningenabled
industrial plants to build a step towards developing
this 6S technology. We develop a modular-based system that can be adapted to the vertical-specific elements. Without loss of generalisation, exemplary use cases are developed and presented including a fault detection/prediction scheme, and a sensor
density-based boundary between orthogonal and non-orthogonal transmissions. The proposed schemes and modelling approach are implemented in a real chemical plant for testing purposes, and a high fault detection and prediction accuracy is achieved
coupled with optimised sensor density analysis.