The Role of Futureproofing in the Management of Infrastructural Assets

Ensuring long-term value from infrastructure is essential for a sustainable economy. In this context, futureproofing involves addressing two broad issues: i. Ensuring the ability of infrastructure to be resilient to unexpected or uncontrollable events e.g. extreme weather events; and ii. Ensuring the ability to adapt to required changes in structure and / or operations of the infrastructure in the future e.g. expansion of capacity, change in usage mode or volumes. Increasingly, in their respective roles, infrastructure designers/builders and owners/operators are being required to develop strategies for futureproofing as part of the life cycle planning for key assets and systems that make up infrastructure. In this paper, we report on a preliminary set of studies aimed at exploring the following issues related to infrastructure / infrastructure systems: • What is intended by the futureproofing of infrastructural assets? • Why and when to futureproof critical infrastructure? • How can infrastructure assets and systems be prepared for uncertain futures? • How can futureproofing be incorporated into asset management practice? In order to seek answers to the above questions, the Cambridge Centre for Smart Infrastructure and Construction (CSIC) has conducted two industrial workshops bringing together leading practitioners in the UK infrastructure and construction sectors, along with government policy makers. This paper provides an initial summary of the findings from the workshops (part presentation, part working sessions), and proposes a simple framework for linking futureproofing into broader asset management considerations. To begin, an overview of futureproofing and motivate the need for futureproofing infrastructure assets is provided. Following this, an approach to futureproofing infrastructure portfolios is presented that organisations in the infrastructure sector can use. Key barriers to futureproofing are also presented before examining the ISO 55001 asset management standard to highlight the interplay between futureproofing and infrastructural asset management. Finally, different ways by which an effective futureproofing strategy can enhance the value of infrastructure are examined

Challenges in infrastructure asset management

Infrastructure owners are facing a number of challenges in an increasingly difficult economic and political setting, and are seeking novel approaches to are required to meet the demands of operators, shareholders and other stakeholders. Owners are demanding greater value, for less overall cost, from their assets. New technologies enable higher performance and greater safety, but at a price. Initial purchase costs are rising, leading to longer periods in service. Maintenance requires a more highly skilled, and so more expensive, workforce. This paper summarises the outputs of two industrial workshops carried out in the UK and USA targeted at identifying the major challenges faced by infrastructure owners and operators. These challenges provide guidance to the academic community for directing research activities to address the needs of industry, thus delivering maximum impact

Heuristic optimisation for multi-asset intervention planning in a petrochemical plant

Large infrastructure assets commonly require high intervention costs, but the absence of an effective asset management plan can bring about a massive production loss for a company. Hence, managing these assets is considered a daunting task and is even more complicated if these assets operate collectively to produce an output. This paper explores a pragmatic approach to a multi-asset intervention scheduling problem through a case study of a vessel fleet in a petrochemical plant. After the relationship between the asset configuration and the system output is defined, an optimisation model with an objective to jointly minimise cost and risk is developed. Since the calculation of risk profiles across the fleet requires complex non-linear functions, a genetic algorithm is employed to search for an optimal combination of intervention schedules. Compared to the current run-to-failure strategy, the optimal strategy results in a significant reduction in system failure risk and a substantial improvement in long-term fleet conditions while reducing the total cost

A conceptual framework for the alignment of infrastructure assets to citizen requirements within a Smart Cities framework

There is a growing interest by academics, industry and government to the digitalisation of the built environment and its potential impact on private enterprises, public services and the broader context of society. The UK government and others are aiming to guide and standardise this process by developing an array of standards to support this digitalisation, most notably on Building Information Modelling (BIM) and Smart Cities Framework. Furthermore, the advancement of the Internet of Things (IoT) is creating a highly flexible, dynamic and accessible platform for the exchange capture and of information. There is a risk that all of this information on the built environment is quickly becoming unmanageable, and the value of that information is quickly becoming lost. This paper proposes a smart asset alignment framework that aims to create an alignment between the information captured at the infrastructure asset level and citizen requirements within a smart city framework

Social Internet of Industrial Things for Industrial and Manufacturing Assets

The IoT (Internet of Things) concept is being widely discussed as the major approach towards the next industry revolution - Industry 4.0. As the value of data generated in social networks has been increasingly recognised, the integration of Social Media and the IoT is witnessed in areas such as product-design, traffic routing, etc.. However, its potential in improving system-level performance in production plants has rarely been explored. This paper discusses the feasibility of improving system-level performance in industrial production plants by integrating social network into the IoT concept. We proposed the concept of SIoIT (Social Internet of Industrial Things) which enables the cooperation between assets by sharing status data and optimal operation and maintenance decision-making via analysis of these data. We also identified the building blocks of SIoIT and characteristics of one of its important components - Social Assets. Related existing work is studied and future work towards the actual implementation of SIoIT is then discussed

A Condition-Based Maintenance Model for Assets with Accelerated Deterioration Due to 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, which is a phenomenon called fault propagation. In this paper, we present 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.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TR.2015.243913

Predictive group maintenance for multi-system multi-component networks

Predictive maintenance has become highly popular in recent years due to the emergence of novel condition monitoring and data analysis techniques. However, the application of predictive maintenance at the network-level has not seen much attention in the literature. This paper presents a model for predictive group maintenance for multi-system multi- components networks (MSMCN). These networks are composed of multiple systems that are, in turn, composed of multiple components. In particular, the hierarchical structure of the MSMCN enables different representations of dependences at the network and system levels. The key novelty in the paper is that the designed approach combines analytical and numerical techniques to optimize the predictive group maintenance policy for MSMCNs. Moreover, we introduce a genetic algorithm with agglomerative mutation (GA-A) that enables a more effective evolution of the predictive group maintenance policy. Application of this model on a case study of a two-bridge network made of 23 different components shows a potential 11.27% reduction in maintenance cost, highlighting the model’s practical significance.This research was funded by the Engineering and Physical Sciences Research Council (UK) and Innovate UK through the Innovation and Knowledge Centre for Smart Infrastructure and Construction (Grant EP/N021614/1). This work was partially supported by Talent recruitment Funds of Tsinghua University grant NO.113052