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SYSTEM OPERATIONS


4G/5G/WiFi


System Sensors


Industrial bus wireless link fibre optics


IoT network Controller


PCB designed Communicator


Tablet/PC Screen HMI 4G/5G


Cloud server


Data storage Signal conditioning


•Data cleaning •Synchronisation •Segmentation


Feature extraction


•Time domain •Frequency domain •Time-frequency domain


Analytics engine


•Health assessment •Performance optimisation •Predictive maintenance


Web services


Analytics database


IP network


AI and machine learning


Signal profile


Feature library


Model library


Real-time machine learning system.


product deliverable) to on-site remote monitoring station/tablet PC screen.


3 4G/5G (mobile network) primarily for off-site remote monitoring.


4 Ethernet (IP network) primarily for off-site remote monitoring.


Real-time data acquisition, analytics and remote monitoring 1. PCB boards (product deliverable) as machine controllers, signal collectors and transmitters.


2 Software for processing real-time operational data and display in a variety of formats, e.g. mimic diagrams, graphs, charts and histograms (edge computing and distributed cloud).


3 Web services with a database management system developed for real-time data remote view, alarms and historical trending analysis (cloud computing).


Intelligent control and predictive maintenance by real-time AI analytics and optimisation 1 Intelligent compressed air system developed by the real-time AI algorithm for optimal control with fixed/variable speed compressor (product deliverable).


2 Algorithms to process real-time data for controlling and real-time AI optimisation (edge computing and distributed cloud), where a DNN using an MLP model is trained to identify network coefficients and then the trained DNN is used as intelligent control for medical air plants.


3 Web services with a database management system and computerized maintenance information management system (CMIMS) developed for real-time AI analytics and optimisation, by creating and


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transmitting the program to the controller. Data is transmitted to enable the operator’s control of the technologies (cloud computing, blockchain technique) and predictive maintenance planning and scheduling, so that we can have a knowledge base from which to correct errors and improve efficiency (software deliverable).


4 Intelligent systems to provide full site history, training records, breakdown records, site closures, equipment replacements, secure data communication and optimal control strategies with energy-saving, predictive maintenance planning and scheduling, and everything else required to enable hospitals fully to meet health and safety reporting requirements.


The intelligent system using advanced technologies could be used in hospitals to operate existing fixed speed medical air compressors more economically than variable speed compressors, thus (i) generating significant energy savings from existing plants, (ii) avoiding the need for capital investment in new variable speed compressors, and (iii) saving operational costs as fixed speed compressors have lower running costs. A fixed speed compressor can be designed to achieve similar performance to a variable speed compressor in terms of energy saving and to overcome the drawbacks of variable speed drive. With blockchain resistant to modification of the data, the real-time intelligent system can also secure data transfer over IoT networks for remote control optimised by deep learning. Our innovative technologies have been used to design a new integrated system to allow remote monitoring, intelligent control and


predictive maintenance to optimise the efficient use of MGS and meet next- generation needs. Hospitals can now be equipped with cutting-edge products, systems and services which will improve productivity and profitability.


Conclusion Insights provided by real-time data from various sensor networks and intelligent systems can offer ways to optimise the operations and maintenance of the physical assets, systems and processes of MGS. SHJ’s development of real-time machine learning systems using big data analytics with edge computing and distributed cloud will help enhance and expand their application widely and allow continuing delivery of the latest technological innovations. This is a new data-driven model that is


dramatically improving the landscape of the healthcare sector. In particular, these advancements will work to alleviate the effects of the health crisis sparked by the outbreak of novel coronavirus. The modern MGS created by such extensive research and development efforts will undoubtedly benefit the healthcare industry and increase our collective expertise, allowing us all to serve the public better.


References 1 Li G, Kong J, Jiang G, Xie L. Research of intelligent control of air compressor at constant pressure. Journal of Computers 2012; 7 (5): 1147-54.


2 Rice AT, Li PY, Sanckens CJ. Optimal efficiency-power tradeoff for an air compressor/expander. Journal of Dynamic Systems, Measurement, and Control 2017; 140 (2): 021011.


3 Ellahia RM, Khanb MUA, Shah A. Redesigning curriculum in line with Industry 4.0. Procedia Computer Science 2019; 151: 699–708.


IFHE DIGEST 2021


IFHE


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