Integration of cost-risk assessment of denial of service within an intelligent maintenance system

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 industrie

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

Methods Used to Support a Life Cycle of Complex Engineering Products

Management of companies involved in the design, development and operation of complex engineering products recognize the relevance of creating systems for product lifecycle management. A system of methods is proposed to support life cycles of complex engineering products, based on fuzzy set theory and hierarchical analysis. The system of methods serves to demonstrate the grounds for making strategic decisions in an environment of uncertainty, allows the use of expert knowledge, and provides interconnection of decisions at all phases of strategic management and all stages of a complex engineering product lifecycle

Plausible Petri nets as self-adaptive expert systems: A tool for infrastructure asset monitoring

This article provides a computational framework to model self-adaptive expert systems using the Petri net (PN) formalism. Self-adaptive expert systems are understood here as expert systems with the ability to autonomously learn from external inputs, like monitoring data. To this end, the Bayesian learning principles are investigated and also combined with the Plausible PNs (PPNs) methodology. PPNs are a variant within the PN paradigm, which are efficient to jointly consider the dynamics of discrete events, like maintenance actions, together with multiple sources of uncertain information about a state variable. The manuscript shows the mathematical conditions and computational procedure where the Bayesian updating becomes a particular case of a more general basic operation within the PPN execution semantics, which enables the uncertain knowledge being updated from monitoring data. The approach is general, but here it is demonstrated in a novel computational model acting as expert system for railway track inspection management taken as a case study using published data from a laboratory simulation of train loading on ballast. The results reveal selfadaptability and uncertainty management as key enabling aspects to optimize inspection actions in railway track, only being adaptively and autonomously triggered based on the actual learnt state of track and other contextual issues, like resource availability, as opposed to scheduled periodic maintenance activities.Lloyd'sRegister Foundation, Grant/Award Number: RB4539; Engineering and Physical SciencesResearch Council, Grant/Award Number:EP/M023028/

Proposing an Optimum Model for Time Estimation of Construction Projects in Iranian Gas Refineries

Time management can be effective in a project when the project schedule is based on comprehensive time scheduling. In the industries with complicated processes, many uncertainties and risks affect the timing of projects. Considering the very low reliability of the project planning in certainty-based approach, using more secure models for control and interact with uncertainty should be placed on the agenda. Iranian Gas Company has been using risk management to manage probable uncertainties in construction projects but in the field of possible uncertainties, actions are very scarce. This article aims to propose an optimum model based on the integrated risk management and fuzzy expert systems in order to provide comprehensive project time estimation and in this regard, reviews the results of the implementation of this model in construction projects of Iranian gas refineries. The results show that the proposed model increases the accuracy of time estimation about 8 to 24 percent.Time management can be effective in a project when the project schedule is based on comprehensive time scheduling. In the industries with complicated processes, many uncertainties and risks affect the timing of projects. Considering the very low reliability of the project planning in certainty-based approach, using more secure models for control and interact with uncertainty should be placed on the agenda. Iranian Gas Company has been using risk management to manage probable uncertainties in construction projects but in the field of possible uncertainties, actions are very scarce. This article aims to propose an optimum model based on the integrated risk management and fuzzy expert systems in order to provide comprehensive project time estimation and in this regard, reviews the results of the implementation of this model in construction projects of Iranian gas refineries. The results show that the proposed model increases the accuracy of time estimation about 8 to 24 percent

Spatial Technologies: What use are they to managers of environmental lands?

This paper reviews the potential use of three types of spatial technology to land managers, namely satellite imagery, satellite positioning systems and supporting computer software. Developments in remote sensing and the relative advantages of multispectral and hyperspectral images are discussed. The main challenge to the wider use of remote sensing as a land management tool is seen as uncertainty whether apparent relationships between biophysical variables and spectral reflectance are direct and causal, or artefacts of particular images. Developments in satellite positioning systems are presented in the context of land managers’ need for position estimates in situations where absolute precision may or may not be required. The role of computer software in supporting developments in spatial technology is described. Spatial technologies are seen as having matured beyond empirical applications to the stage where they are useful and reliable land management tools. In addition, computer software has become more user-friendly and this has facilitated data collection and manipulation by semi-expert as well as specialist staff

What are we Managing Anyway?: The Need for an Interdisciplinary Approach to Managing Fisheries Ecosystems

Fisheries managers should really be attempting to manage the fishing fleets and the processing industry, not the fish. Consequently we argue that effective management ought to take an eco-systems approach that is necessarily interdisciplinary, incorporating both natural and social sciences. We ascribe the inadequate results of existing management regimes to scientific uncertainty, political pressures, the regulations\u27 lack of legitimacy among fishers, and excessive reliance on individual fishers (rather than households and communities) as the unit of analysis. In a new interdisciplinary approach, we emphasize the contribution of social science in helping to understand what is defined as Scientific knowledge, how expert scientific and local or traditional knowledge might be integrated, and the role of science in the management process. We conclude by advocating an ecosystem management strategy of periodic (every three to five years) in-depth assessments with explicit requirements for sociological and economic input

Knowledge-based approaches for river basin management

International audienceRare attempts to use knowledge technologies and other relevant approaches are found in the river basin management. Some applications of expert systems as well as utilization of soft computing techniques (as neural networks or genetic algorithms) are known in an experimental level. Knowledge management approaches still have not been used at all. In this paper we discuss knowledge-based approaches in the river basin management as a difficult yet important direction which could be proven to be helpful. We summarize the research done in the scope of the AQUIN project, one of first Czech knowledge management projects in the river basin management. The project was initiated by the water management company in Pilsen, where dispatchers make decisions about manipulations on the reservoir Nýrsko, the strategic source of drinking water for inhabitants of Pilsen. The project aim was to support dispatchers' decision making under a high degree of uncertainty or data shortage. The research is continued in the scope of a new project AQUINpro, planned for the period of 2006 to 2008