Zhenglin Liang

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. The aim of the policy is to replace the malfunctioned component and mitigate accelerated deterioration at minimal impact to the business. The maintenance model provides guidance on determining inspection and maintenance strategies to optimize asset availability and operational cost.

Wenrong Lu

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? 

Having proposed an interventionist routing algorithm to enable the dynamic re-routing of an order-picker during the picking operation, Wenrong’s project now investigates the dynamism from making the three decisions in different sequences.  By formulating the problem as a Markov Decision Process (MDP), Wenrong aims to develop a method of making the decisions in appropriate sequence based on the status of the operation so as to improve the flexibility as well as the efficiency of the order-picking operation.

Torben Jess

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. The user has a certain utility (or value) for specific datasets or dataset combinations. At the same time datasets have costs. Markets like supermarkets combine these two in an efficient manner. Consumers in a supermarket have a specific value for certain products and the supermarket has costs associated in offering these. The market approach is working quite efficiently in various applications and has been shown to work well for similar resource allocation problems. By applying this approach towards data management, Torben is hoping to improve the user decision-making by providing him with the right information and to identify a value for companies large amounts of datasets.

JQ Wang

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. There is a very limited number of existing studies on the proposed topic, and the mainstream approach in generic performance measurement literature is not the most suitable in the context of organisations’ EAM. A crucial reason is that risk control is not included as independently essential perspective. However EAM heavily relies on the successful management of various risks such as asset safety, reliability and many other potential hazards. Furthermore, the complexity and scope of EAM is quite difficult to be modelled in the performance measurement, therefore leading literature and practical experience of EAM should be adapted in the design process to understand the full picture of EAM. JQ Wang has proposed frameworks by refining existing approach of designing performance measures for asset-intensive organisations’ EAM. Risk control elements and leading EAM knowledge will be factor in the design process to assist organisations to select their performance measures holistically. JQ will apply three phase case studies including a facilitator case study for validating the research. His pilot case study has proved that the frameworks are usable and feasible for partner organisations to review and improve existing performance measures for their EAM.

Joel Adams

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. These factors/criteria will depend upon asset operating condition, business environment, maintenance objectives and key performance indicators of the organisation. The algorithm should detect changes in criticality, connect to the company’s enterprise asset management system to automatically reproduce the analysis and update criticality accordingly. From this research, Joel is hoping to automatically adjust maintenance program to business needs by exploiting the dynamic nature of criticality to generate dynamic CBM strategies.