A value-based approach to optimizing long-term maintenance plans for a multi-asset k-out-of-N system

Devising a long-term maintenance plan for a system of large infrastructure assets is an exacting task. Any maintenance activity that induces system downtime can incur a massive production or service loss. This problem becomes increasingly challenging for a system of which the performance is based on the collective output of assets. Current approaches that optimise each asset in isolation or consider a binary performance relationship insufficiently address this issue because the negligence of performance interactions among assets results in an inaccurate cost estimation. To overcome these hurdles, we formulate a mathematical model that explicitly demonstrates dynamic risk of production loss according to the system aggregate output. Further, we propose an integrated solution method that couples a finite loop search with a Genetic Algorithm. Application of our model to a real-world case study has proved to simultaneously strike the balance between cost and risk. Validated by Monte Carlo simulation, the proposed model has shown to outperform existing approaches. By systematically scheduling maintenance actions over the planning horizon, the resultant strategy has demonstrated to offer considerable maintenance cost savings and significantly prolong the average asset life. Sensitivity analyses also evince the robustness of the proposed model under the volatility in key parameters.EPSRC (This does not appear on the submitted manuscript yet, but will be added in the final proof

A value-based approach to optimizing long-term maintenance plans for a multi-asset k-out-of-N system

Devising a long-term maintenance plan for a system of large infrastructure assets is an exacting task. Any maintenance activity that induces system downtime can incur a massive production or service loss. This problem becomes increasingly challenging for a system of which the performance is based on the collective output of assets. Current approaches that optimize each asset in isolation or consider a binary performance relationship insufficiently address this issue because the negligence of performance interactions among assets results in an inaccurate cost estimation. To overcome these hurdles, we formulate a mathematical model that explicitly demonstrates dynamic risk of production loss according to the system aggregate output. Further, we propose an integrated solution method that couples a finite loop search with a Genetic Algorithm. Application of our model to a real-world case study has proved to simultaneously strike the balance between cost and risk. Validated by Monte Carlo simulation, the proposed model has shown to outperform existing approaches. By systematically scheduling maintenance actions over the planning horizon, the resultant strategy has demonstrated to offer considerable maintenance cost savings and significantly prolong the average asset life. Sensitivity analyses also evince the robustness of the proposed model under the volatility in key parameters

Asset Management Framework and Tools for Facing Challenges in the Adoption of Product-Service Systems

Servitisation is recognized as a key business strategy for Original Equipment Manufacturers willing to move up the value chain. However, several barriers have to be overcome in order to successfully integrate products and services. Many of these barriers are caused by the technical challenges associated with the design and management of the product-service systems, such as life cycle service level and cost estimation, risk management, or the system design and pricing. Asset management presents itself as a key research area in order to overcome these barriers as well as to integrate product-service systems within the manufacturers’ operations management. It is the scope of this paper to provide theoretical and practical insights with regards to the alignment of asset management and product-service system research areas. To support the alignment between both areas, a management framework which gathers specific technologies, including reliability analysis, simulation modelling and multi-objective optimisation algorithms, is presented. The purpose of the framework is to provide manufacturers with a decision-support tool that facilitates the main managerial challenges faced when implementing a servitisation strategy. The paper contributions are successfully applied to case studies in the railway and wind energy sectors based on real field data, thereby demonstrating their suitability for both facilitating manufacturer’s decision making process and better satisfying stakeholders’ interests.This research work was performed within both 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, MSCA-RISE-2014: Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) (grant agreement number 645733 — Sustain-Owner — H2020-MSCARISE-2014) and the EmaitekPlus 2018-2019 Program of the Basque Government

Opportunistic maintenance for wind turbines considering external opportunities - A case study

This paper aims to develop an opportunistic maintenance (OM) policy for the generator of a hypothetical wind turbine using methods developed recently by the authors. The OM policy considers external opportunities caused by low wind speeds which produce little-to-no electric power. The results show that some cost savings are achievable by taking maximal advantage of these low-speed wind events, particularly when electricity prices are at their peak cycle