Current PhD Projects
Maintenance optimisation for multi-component asset with fault propagation
Complex industrial assets such as power transformers are subject to accelerated deterioration when one of its constituent component malfunctions, affecting the condition of other components – a phenomenon called ‘fault propagation’. Zhenglin’s research is to design a novel approach for optimizing condition-based maintenance policies for such assets by modelling their deterioration as a multiple dependent deterioration path process.
Dynamic Order-picking strategy
Today’s warehouses are faced with new challenges that require strategies that can offer more flexibility than conventional strategies are able to do. In this context, Wenrong’s PhD project aims to address the flexibility challenge by investigating how order-picking, which is a key factor affecting warehouse performance, can be dynamically managed. The order-picking operation is typically constrained by inventory management in the warehouse and transportation management of placed orders. More specifically, the order-picking operation is managed based on three key decisions:
a) When should the orders be picked from the warehouse?
b) Which storage location should the order-picker visit?
c) How should the orders be batched together to form a pick-list?
Active market based industrial data management
Torben's Phd project is addressing problems of data overload and the Value of Information. Currently most information systems specifically allocate data to a specific user. However, the amount of data and the number of tasks each user has to do are constantly increasing at a very large rate. Therefore, companies are having a massive data overload problem and ensuring the right data is getting to the right users can be difficult for them. Torben tries to address this problem by using market-based techniques, which use the principle of markets in economics.
Performance measurement in engineering asset management systems
The engineering asset performances such as reliability and maintainability directly impact the ultimate overall business performance. Therefore asset intensive manufacturing companies heavily rely on their engineering asset management systems to gain core competitive advantages. However, developing effective performance measures for valuable and complicated engineering asset management (EAM) has always been a challenge for asset intensive manufacturing organisations. Additionally having effective performance measures in place is required by a number of international standards on engineering asset management such as ISO55000 and PAS55.
Criticality of assets
Joel’s research seeks to address the dynamic nature of assets’ criticality. So far criticality analysis, which is a tool for deciding what assets should have priority within a maintenance management program, has been treated as a static concept both in literature and in practice. The myth is: “…we have just concluded our criticality analysis; we can now check that box...” But insufficient understanding of the changing nature of criticality has led to misalignment between asset maintenance strategies and the business goals of the organisation over time. Joel attempts to develop a model that will combine several multi criteria decision making techniques to identify factors that influence changes in criticality.
Quantifying the Impact of Additive Manufacturing in Production Operation Responsiveness
In the existing manufacturing environment, the unexpected events are causing disruptions that are “negative” (machine breakdown or delayed/defective supply of parts to manufacturers) but more increasingly “positive” (a rush order from customer, or a late customisation request). This is resulting in both the loss of customers as well as production losses. Owing to the inherent capabilities of the so-called “factory-in-the-box”, a 3D printer can address “negative” but more importantly “positive” disruptions that are primarily driven by customers (‘responsiveness’).
Mudassar attempts to develop a model that will quantify and represent how the rapid manufacturing capabilities offered by AM influence the responsiveness (flexibility, efficiency, effectiveness etc) of a production system, which will help find a place (in favour or against) this so-called disruptive technology.