The use of condition monitoring and Internet of Things (IoT) communication devices have become crucial in modern railway businesses. They do not only provide real-time data visualization and monitoring for important service-related components, but also safeguard service quality and minimize failures with the support of prescriptive maintenance. A non-invasive real time monitoring device has been invented and developed to measure data for train-borne relays. The design employs a high calibre microprocessor, IoT Long Range (LoRa) infrastructure along trackside and station and the transmission of data to IoT maintenance cloud platform with 4G wireless connection. With self-implemented hardware & software, the device can be adjusted easily based on different needs, ensuring flexibility in applications ranging from trainborne to track-side maintenance. The solution is also fully automatic, requiring minimum installation manpower and is physically isolated from the monitored equipment. Compare with conventional railway maintenance, ARMA allows timely prediction of equipment’s deterioration rate during traffic hours through analysis with in-house maintenance cloud system, making it possible to perform inspection & replacement before breakdown. Prototypes for the proof of concept with LoRa transmission capability via leaky coaxial infrastructure have been developed for the sake of seamless and low-cost connectivity even inside tunnels. With multiple tests conducted in workshops and on-site, the device will become a part of the newly developed IoT LoRa infrastructure of the railway network, highlighting the significance of fast response and automated IoT intelligent solutions in the railway industry.
Rail corrugation is a common type of wear in all tracks. With the rising axle loads and train speeds for modern railway administrations, the demand of early detection has taken prominence. With new methodologies and algorithms arising, it remains a challenge for industries to decide which tool is the best fit. This project proposed a machine learning architecture for the detection of rail corrugation. The software developed in this project is based on the dataset obtained from an advanced FBG sensing system. A data pre-processing software was developed to extract signatures from the interaction of train wheels and rail. A J48 decision tree algorithm followed by advanced sampling techniques were utilized to ascertain its level of influence on the formation of corrugation. The problem was then modelled using various classifiers and their performance was evaluated by comparing their classification accuracies. The results demonstrate the effectiveness of the proposed approach with 97% accuracy. Hence, this method is fit for industrial application and its universal setting makes it applicable to various engineering disciplines. In short, the proposed approach offers robust performance advantages and, as fault detection being the preliminary stage of predictive maintenance, it also establishes a strong foundation for future development.
The Built Environment Application Platform (BEAP) takes forward the Common Spatial Data Infrastructure (CSDI) development strategy and provides a centralized platform for the development of applications covering aspects on city planning, infrastructure/engineering and environment to improve efficiency, transparency and support for decision making in planning and development, fostering interdepartmental cooperation and synergy. To tie in with the rapid urbanization around the world, the BEAP provides a valuable tool for users to help shape a better world and provide high quality services. The BEAP is developed on a cloud-based GIS solution to accommodate for different components to avoid single point of failure and enable future expansion. With the innovative use of various actionable datasets, the team developed 10 prototype applications to demonstrate the capabilities of the BEAP utilizing a digital twin approach and emerging technologies such as AI and BIM. In fact, the BEAP has already brought a significant amount of savings for its immediate users, which can further benefit a wide spectrum of stakeholders, from city-wide management to individual building owners, and ultimately citizens. It is envisioned that the BEAP can extend its services to not only the local Hong Kong context, but also to other cities in the world.
As the world struggles to address the recent pandemic there is an immediate need to develop affordable and sustainable solutions to improve the whole-life monitoring of new and existing building structures.
Although Modular Systems are an existing technique to fast-track production whilst providing a safer environment during manufacturing, their application in the construction industry is not widespread.
Team Modu-vation acknowledges that the Services in structures do not last the structure’s lifetime and this leads to early obsolescence and operational risk.
Using the concept of modularisation of building elements, we propose the development of Modu-Link comprising Modu-Struct, Modu-Serve and Modu-Sense to efficiently & economically manage the whole life of building structures. The Modu-Struct connection system is newly developed to address the major robustness issues criticised for modular structures. Modu-Serve allows easy installation and removal of MEP systems by implementing the use of push-fit fittings and mechanical joint couplings. Modu-Sense will report on real-time vital data from the MEP system allowing predictive maintenance and reduced downtime risk. Modu-Sense’s extensibility will be accommodated remotely by software updates.
Team Modu-vation believes that this combined IoT system will be the key driver for the present and the future of healthy buildings.
For passenger aircrafts, the amount of flushing water was significantly reduced by the use of vacuum flushing. However, some flushing water is still needed, and the recirculated use of the water is necessary, which may lead to odor & infection concerns. It is also suspected that the during toilet flushing, even by vacuum flushing, some vapours may still escape into the lavatory. The idea of this project to examine the existing aircraft and other toilet design features for odor & infection control. One possible mechanism is to allocate part of the existing exhaust air through the toilet interior so that the toilet odour/infectious particles would not escape in the lavatory. Other possible development would be also given out.
Island in Blue – a project revealing the possibility of future living style on the ocean.
Facing the serious global threat – sea level rise, due to the warming climate, engineers need to plan ahead for the future challenge. Island in Blue is a “floating island” in the sea to live in the future serving as a lifeboat for people living in low-lying areas. This project performs as a small pilot scheme discovering the feasibility.
The pilot Island can not only be a trial for the future living in the long run, but also facilitate the investigation works of ocean scientists or any other professionals to stay in the sea for a long period of time. Economically, the pilot Island can turn into a hotel for boosting tourism and economic activities in the coastal area.
On the Island, a major power source is solar energy provided by sufficient sunshine on the sea. The green technologies like smart glass, grey water reuse, etc. are applied. With the use of renewable energy and green technology, Island in Blue provides a low-to-zero carbon lifestyle as a self-sufficient structure.
Circuit Breaker (CB) is one of the most critical assets in our transmission and distribution network. Any failure of CB will result in severe supply interruption to the customers. So far, unless it is switched out for inspection and maintenance, there isn’t an all in one, robust and practical method to monitor the condition of CB continuously and identify the CB with incipient defect. Our project focuses on designing a tailored-made solution for Online Circuit Breaker Monitoring System (OCBMS) for a fleet of over one thousand CBs by monitoring the current profile of the trip coil of CBs. Through a statistical model and machine learning approach, circuit breakers with abnormal performance can be spot out and preventive action can be made immediately before the breakdown of those circuit breakers. The reliability of our distribution network can significantly be improved through our cost effective design.