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

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

An approach to value based infrastructure asset management

Effective management of civil infrastructure assets requires intricate considerations with regard to safety, serviceability, reputation and cost. Additionally, infrastructure assets have different requirements from various stakeholders and have a longer service life. However, traditional asset management decisions focused predominantly on cost, and there is an inherent need to understand the value of an infrastructure asset to various stakeholders and to utilise this value to drive asset management decisions. In this paper, a systematic approach to making value-based asset management decisions is proposed. The proposed process provides an efficient method for mapping the stakeholder’s requirements to the value provided by the asset. This map can then be used to assess the value and make effective decisions. The developed approach is demonstrated through a case study involving transportation tunnels. The essential consideration of value is expected to allow organisations to evaluate the balance between cost, risk and performance, thereby allowing better-informed decisions.The research work was performed within the context of SustainOwner (‘Sustainable Design and Management of Industrial Assets through Total Value and Cost of Ownership’), a project sponsored by the EU Framework Programme Horizon 2020, MSCARISE-2014: Marie Skłodowska-Curie Research and Innovation Staff Exchange (Rise) (grant agreement number 645733 – Sustain-owner – H2020-MSCA-RISE-2014). The research was funded by the Engineering and Physical Sciences Research Council Innovation and Knowledge Centre for Smart Infrastructure and Construction at the University of Cambridge

Dynamic maintenance based on criticality in electricity networks

The need to prioritize maintenance activities and investments based on asset criticality and associated risk is seen increasingly as important in industry. However, proper use of criticality in developing maintenance strategies and plans is still at a nascent stage in most organisations. A review of industrial practices showed that criticality is considered as more or less a static quantity that is not updated with sufficient frequency as the operating environment changes. This paper examines an electricity distribution network operator (DNO) to show the need to model the changing nature of criticality and ensure an optimal maintenance strategy and plan, aligned to business needs. A Dynamic Criticality Based Maintenance (DCBM) methodology is proposed to identify factors affecting and influencing changes to criticality, monitor and update asset criticality and exploit the dynamic criticality to optimise maintenance decisions. Asset criticality was calculated using network performance, safety, environmental integrity, maintenance cost among other factors as the consequence categories for asset failure. The criticality for each asset (such as transformer circuit breakers, busbars etc.) is calculated as a weighted sum of the impact of supply loss on each of the consequence categories. Variations in some factors such as electricity demand influences changes in asset criticality with time and therefore criticality is modelled as a dynamic process, which is a function of time in addition to other factors. The performance measure used for the maintenance plan is based on the utility network reliability (quality of service) which is measured in terms of Customer Interruptions (CI) and Customer Minutes Lost (CML). The performance targets (for CIs & CMLs) and standard service levels for DNOs are given in the UK's Office of Gas and Electricity Markets (OFGEM). The results showed evidence of changing asset criticality in the network. The benefit of reviewing maintenance plans based on changing criticality was also highlighted

Exploiting traffic data to improve asset management and citizen quality of life

The main goal of this project was to demonstrate how large data sources such as Google Maps can be used to inform transportation-related asset management decisions. Specifically, we investigated how the interdependence between infrastructures and assets can be studied using transportation data and heat maps. This involves linking the effect of disruptions in lower-order assets to travel accessibility to private and public infrastructure. In order to demonstrate the viability of our approach, we conducted 5 case studies, 3 public and 2 private. On the public side, we collaborated with two county councils in the United Kingdom, specifically Cambridgeshire and Hertfordshire, and offered solutions to existing infrastructure-related problems proposed by them. For Cambridgeshire, we analysed the accessibility to Cambridge University’s new research centers and the criticality of roads leading to Addenbrooke’s Hospital in Cambridge. Similarly for Hertfordshire, the accessibility to different critical assets in the county were examined with the aim of supporting planning decisions. In addition, to highlight how our approach can bring benefits to private citizens, we solved two examples of commuting-related problems posed by students at the Institute for Manufacturing (IfM). We conclude that heat maps generated using the Google Maps API are powerful and efficient tools for use in infrastructure asset management. Our approach appears to be more cost-efficient and offers a higher quality of visualisation and presentation than other available tools. Furthermore, there exists the potential for a commercial spin-off: our approach can be employed in local, regional and national administrations to inform infrastructure-related decision-making, and can be used by commercial parties to improve employees’ commutes, parking, et ceteraCentre for Digital Built Britai

Study of dynamic workload assignment strategies on production performance

As maintenance has grown to be seen as a prospective tool for production value generation and business performance improvement, it can no longer be considered as isolated from other production activities. Studies have shown that the degradation process of machines is dependent on the operation being performed (e.g., higher workload results in faster degradation). However, the decision-making in maintenance planning with dynamic operation/workload adjustment considerations has not been addressed until recently. Moreover, the existing approaches attempting to tackle this problem have overlooked the fact that dynamics exist in both external production environment and internal production conditions and thus prove to be inefficient to react to unexpected situations arising. This paper has explored the impacts of different workload adjustment strategies on system production performance by a numerical study using agent-based simulation. A detailed discussion is given on the implication of the simulation outcome, based on which some insights into potential future work are also presented.EU H2020 Acknowledgments to financial support of Cambridge Trust and China Scholarship Counci

Towards Dynamic Criticality-Based Maintenance Strategy for Industrial Assets

An asset’s risk is a useful indicator for determining optimal time of repair/replacement for assets in order to yield minimal operational cost of maintenance. For a successful asset management practice, asset-intensive organisations must understand the risk profile associated with their asset portfolio and how this will change over time. Unfortunately, in many risk-based asset management approaches, the only thing that is known to change in the risk profile of the asset is the likelihood (or probability) of failure. The criticality (or consequences of failure) of asset is assumed to be fixed and has considered as more or less a static quantity that is not updated with sufficient frequency as the operating environment changes. This paper proposes a dynamic criticality-based maintenance approach where asset criticality is modeled as a dynamic quantity and changes in asset’s criticality is used to optimize maintenance plans (e.g. determining the optimal repair time/replacement age for an asset over it life cycle period) to have a better risk management and cost savings. An illustrative example is used to demonstrate the effect of implementing dynamic criticality in determining the optimal time of repair for a bridge infrastructure. It is shown that capturing changes in the criticality of the bridge over time and using this understanding in the risk analysis of the bridge provided the opportunity for better maintenance planning resulting to reduction of the total risk

On fault propagation in deterioration of multi-component systems

In extant literature, deterioration dependence among components can be modelled as inherent dependence and induced dependence. We find that the two types of dependence may co-exist and interact with each other in one multi-component system. We refer to this phenomenon as fault propagation. In practice, a fault induced by the malfunction of a non-critical component may further propagate through the dependence amongst critical components. Such fault propagation scenario happens in industrial assets or systems (bridge deck, and heat exchanging system). In this paper, a multi-layered vector-valued continuous-time Markov chain is developed to capture the characteristics of fault propagation. To obtain the mathematical tractability, we derive a partitioning rule to aggregate states with the same characteristics while keeping the overall aging behaviour of the multi-component system. Although the detailed information of components is masked by aggregated states, lumpability is attainable with the partitioning rule. It means that the aggregated process is stochastically equivalent to the original one and retains the Markov property. We apply this model on a heat exchanging system in oil refinery company. The results show that fault propagation has a more significant impact on the system's lifetime comparing with inherent dependence and induced dependence

Recurrent Neural Networks for real-time distributed collaborative prognostics

We present the first steps towards real-time distributed collaborative prognostics enabled by an implementation of the Weibull Time To Event - Recurrent Neural Network (WTTE-RNN) algorithm. In our system, assets determine their time to failure (TTF) in real-time according to an asset-specific model that is obtained in collaboration with other similar assets in the asset fleet. The presented approach builds on the emergent field of similarity analysis in asset management, and extends it to distributed collaborative prognostics. We show how through collaboration between assets and distributed prognostics, competitive time to failure estimates can be obtained

A Multi Agent System architecture to implement Collaborative Learning for social industrial assets

The `Industrial Internet of Things' aims to connect industrial assets with one another and subsequently bene t from the data that is generated, and shared, among these assets. In recent years, the extensive instrumentation of machines and the advancements in Information Communication Technologies are re-shaping the role of assets in our industrial systems. An emerging paradigm here is the concept of `social assets': assets that collaborate with each other in order to improve system performance. Cyber-Physical Systems (CPS) are formed by embedding the assets with computing capabilities and linking them with their cyber models. These are known as the `Digital Twins' of the assets, and form the backbone of social assets. Collaboration among assets, by allowing them to share and analyse data from other assets can make embedded computing algorithms more accurate, robust and reliable. This paper proposes a Multi Agent System (MAS) architecture for collaborative learning, and presents the fi ndings of an implementation of this architecture for a prognostics problem. Collaboration among assets is performed by calculating inter-asset similarity during operating condition to identify `friends' and sharing operational data within these clusters of friends. The architecture described in this paper also presents a generic model for the Digital Twins of assets. Prognostics is demonstrated for the C-MAPSS turbofan engine degradation simulated data-set (Saxena and Goebel (2008))