• An autonomous system for maintenance scheduling data-rich complex infrastructure: fusing the railways’ condition, planning and cost

      Durazo-Cardenas, Isidro; Starr, Andrew; Turner, Christopher J.; Tiwari, Ashutosh; Kirkwood, Leigh; Bevilacqua, Maurizio; Tsourdos, Antonios; Shehab, Essam; Baguley, Paul; Xu, Yuchun; et al. (Elsevier, 2018-02-22)
      National railways are typically large and complex systems. Their network infrastructure usually includes extended track sections, bridges, stations and other supporting assets. In recent years, railways have also become a data-rich environment. Railway infrastructure assets have a very long life, but inherently degrade. Interventions are necessary but they can cause lateness, damage and hazards. Every day, thousands of discrete maintenance jobs are scheduled according to time and urgency. Service disruption has a direct economic impact. Planning for maintenance can be complex, expensive and uncertain. Autonomous scheduling of maintenance jobs is essential. The design strategy of a novel integrated system for automatic job scheduling is presented; from concept formulation to the examination of the data to information transitional level interface, and at the decision making level. The underlying architecture configures high-level fusion of technical and business drivers; scheduling optimized intervention plans that factor-in cost impact and added value. A proof of concept demonstrator was developed to validate the system principle and to test algorithm functionality. It employs a dashboard for visualization of the system response and to present key information. Real track incident and inspection datasets were analyzed to raise degradation alarms that initiate the automatic scheduling of maintenance tasks. Optimum scheduling was realized through data analytics and job sequencing heuristic and genetic algorithms, taking into account specific cost & value inputs from comprehensive task cost modelling. Formal face validation was conducted with railway infrastructure specialists and stakeholders. The demonstrator structure was found fit for purpose with logical component relationships, offering further scope for research and commercial exploitation. ​​​​​​​
    • Challenges in cost analysis of innovative maintenance of distributed high value assets

      Kirkwood, Leigh; Shehab, Essam; Baguley, Paul; Amorim-Meloa, P.; Durazo-Cardenas, Isidro; Cranfield University (Elsevier, 2014-10-31)
      Condition monitoring is an increasingly important activity, but there is often little thought given to how a condition monitoring approach is going to impact the cost of operating a system. This paper seeks to detail the challenges facing such an analysis and outline the likely steps such an analysis will have to take to more completely understand the problem and provide suitable cost analysis. Adding sensors might be a relatively simple task, but those sensors come with associated cost; not only of the sensor, but of the utilities required to power them, the data gathering and processing and the eventual storage of that data for regulatory or other reasons. By adding condition monitoring sensors as a subsystem to the general system an organisation is required to perform maintenance to the new sensors sub-system. Despite these difficulties it is anticipated that for many high value assets applying condition monitoring will enable significant cost savings through elimination of maintenance activities on assets that do not need such cost and effort expended on them. Further savings should be possible through optimisation of maintenance schedules to have essential work completed at more cost efficient times.
    • Cost drivers of integrated maintenance in high-value systems

      Shehab, Essam; Kirkwood, Leigh; Amorim-Melo, P.; Baguley, Paul; Cranfield University (Elsevier, 2014-10-31)
      High value systems are determined by a wide structure, where operations are considered to be one structural component. Nowadays “downtime” as a major impact in the operation costs of any system. To avoid or minimize “down-time” it is essential to match the appropriate maintenance to each failure. Therefore, it is relevant to determine the cost drivers of integrated maintenance in any system, in order to minimize the overall cost. It is common to use Value Driven Maintenance (VDM) to capture the cost drivers in maintenance. VDM is a methodology which relies in four distinct areas: Asset Utilization; Resource Allocation; Control Cost and Health and Safety and Environment. Within each category it is possible to allocate different cost drivers, building a framework for each system studied. The aim of this paper is to categorize the cost drivers of rail infrastructure networks, associating them with the maintenance preformed for each case. Furthermore, analysis of which part of the track falls under each VDM category as well as the general failure causes and effects will be included in the framework presented. Finally relating the maintenance type for each effect will provide the necessary inputs towards a cost model structure. The benefit of achieving a successful model will be the optimization of the cost in integrated maintenance of the rail infrastructure.
    • COTECHMO: The Constructive Technology Development Cost Model

      Jones, Mark B.; Webb, Phil F.; Summers, Mark D.; Baguley, Paul; Cranfield University (Taylor & Francis, 2014-04-03)
      A detailed analysis of the available literature and the aerospace manufacturing industry has identified a lack of cost estimation techniques to forecast advanced manufacturing technology development effort and hardware cost. To respond, this article presents two parametric ‘Constructive Technology Development Cost Models’ (COTECHMO). The COTECHMO Resources model is the first and is capable of forecasting aerospace advanced manufacturing technology development effort in personhours. When statistically analyzed, this model had an outstanding R-squared value of 98% and a high F-value of 106.65, validating model significance. The general model accuracy was tested with 53% of the forecast data falling within 20% of the actual. The second, the COTECHMO Direct Cost model is capable of forecasting the development cost of the aerospace advanced manufacturing technology process hardware. This model had an inferior R-squared value of 76% and an F-value of 5.59, although each was still valid to determine model significance. However, the Direct Cost model accuracy exceeded the Resources model, with 93% of the forecast data falling within 20% of the actual. The article concludes with recommendations for future research, including suggestions for further enhancement of each model verification and validation, within and outside of the supporting organization. ​​​​​​​
    • Environmental modelling of the Chief Information Officer

      Harding, David J.; Fan, Ip-Shing; University of Bedfordshire; Cranfield University (2017-04-05)
      Since the introduction of the term in the 1980’s, the role of the Chief Information Officer (CIO) has been widely researched. Various perceptions and dimensions of the role have been explored and debated. However, the explosion in data proliferation (and the inevitable resulting information fuelled change) further complicates organisational expectations of the CIOs role. If organisations are to competitively exploit the digital trend, then those charged with recruiting and developing CIOs now need to be more effective in determining (and shaping) CIO traits and attributes, within the context of their own organisational circumstances and in line with stakeholder expectations. CIOs also need to determine their own suitability and progression within their chosen organisation if they are to remain motivated and effective. Before modelling the role of the future CIO, it is necessary to synthesise our current knowledge (and the lessons learnt) about the CIO. This paper, therefore, aims to identify and summate the spectrum of key researched ‘themes’ pertaining to the role of the CIO. Summating previous research, themes are modelled around four key CIO ‘dimensions’, namely (1) Impacting factors, (2) Controlling factors (3) Responses and (4) CIO ‘attributes’. Having modelled the CIOs current environment, and recognising the evolving IT enabled information landscape, the authors call for further research to inform the recruitment and development of the future CIO in terms of personal attributes and the measurable impact such attributes will have on their respective organisation.
    • Integration of cost-risk assessment of denial of service within an intelligent maintenance system

      Carlander, L.; Kirkwood, Leigh; Shehab, Essam; Baguley, Paul; Durazo-Cardenas, Isidro; Cranfield University (Elsevier, 2020-04-29)
      As organisations become richer in data the function of asset management will have to increasingly use intelligent systems to control condition monitoring systems and organise maintenance. In the future the UK rail industry is anticipating having to optimize capacity by running trains closer to each other. In this situation maintenance becomes extremely problematic as within such a high-performance network a relatively minor fault will impact more trains and passengers; such denial of service causes reputational damage for the industry and causes fines to be levied against the infrastructure owner, Network Rail.     Intelligent systems used to control condition monitoring systems will need to optimize for several factors; optimization for minimizing denial of service will be one such factor. With schedules anticipated to be increasingly complicated detailed estimation methods will be extremely difficult to implement. Cost prediction of maintenance activities tend to be expert driven and require extensive details, making automation of such an activity difficult. Therefore a stochastic process will be needed to approach the problem of predicting the denial of service arising from any required maintenance. Good uncertainty modelling will help to increase the confidence of estimates.      This paper seeks to detail the challenges that the UK Railway industry face with regards to cost modelling of maintenance activities and outline an example of a suitable cost model for quantifying cost uncertainty. The proposed uncertainty quantification is based on historical cost data and interpretation of its statistical distributions. These estimates are then integrated in a cost model to obtain accurate uncertainty measurements of outputs through Monte-Carlo simulation methods. An additional criteria of the model was that it be suitable for integration into an existing prototype integrated intelligent maintenance system. It is anticipated that applying an integrated maintenance management system will apply significant downward pressure on maintenance budgets and reduce denial of service. Accurate cost estimation is therefore of great importance if anticipated cost efficiencies are to be achieved. While the rail industry has been the focus of this work, other industries have been considered and it is anticipated that the approach will be applicable to many other organisations across several asset management intensive industries.   
    • Uncertainty of Net Present Value calculations and the impact on applying integrated maintenance approaches to the UK rail industry

      Kirkwood, Leigh; Shehab, Essam; Baguley, Paul; Starr, Andrew; Cranfield University (Elsevier, 2015-10-27)
      The Public performance indicator (PPI) is an important Key Performance Indicator for Network Rail and monitored carefully by the organisation and their external stakeholders. Condition monitoring is of increasing interest within network rail as a suitable method for increasing asset reliability and improving the PPI metric. As condition monitoring methods are identified each will need assessment to establish the cost and benefit. Benefit can be measured in cost savings as poor PPI performance results in fines. Within many industries Net Present Value (NPV) calculations are used to determine how quickly investments will break-even. Cost-risk is a term that is used to describe the financial impact of an unexpected event (a risk). This paper outlines a more detailed approach to calculating NPV which considers the cost-risk effect of changes of the denial of service charging rate. NPV prediction is of importance when assessing when to deploy different fault detection strategies to maintenance issues, and therefore the cost-risk of the NPV calculation should be used to support asset management decisions.