An agent-based implementation of hidden Markov models for gas turbine condition monitoring

This paper considers the use of a multi-agent system (MAS) incorporating hidden Markov models (HMMs) for the condition monitoring of gas turbine (GT) engines. Hidden Markov models utilizing a Gaussian probability distribution are proposed as an anomaly detection tool for gas turbines components. The use of this technique is shown to allow the modeling of the dynamics of GTs despite a lack of high frequency data. This allows the early detection of developing faults and avoids costly outages due to asset failure. These models are implemented as part of a MAS, using a proposed extension of an established power system ontology, for fault detection of gas turbines. The multi-agent system is shown to be applicable through a case study and comparison to an existing system utilizing historic data from a combined-cycle gas turbine plant provided by an industrial partner

A multi-objective optimisation model for sewer rehabilitation considering critical risk of failure

Copyright © 2012 IWA Publishing. The definitive peer-reviewed and edited version of this article is published in Water Science & Technology, Vol 66 No 11, pp. 2410–2417 (2012), DOI: 10.2166/wst.2012.393 and is available at www.iwapublishing.com.A unique methodology for the optimal specification of sewer rehabilitation investment is presented in this paper. By accounting for the critical risk of asset failure, this methodology builds on previously successful work which explored the application of multi-objective optimisation tools to assist engineers with the specification of optimal rehabilitation strategies. The conventional sewerage rehabilitation specification process relies on the expertise of professional engineers to manually evaluate CCTV inspection information when determining the nature and extent of the rehabilitation solution. This process is not only tedious and subjective but it has no quantifiable means of identifying optimal solutions or possible combinations of optimal solutions in the delivery of catchment wide rehabilitation programmes. Therefore, the purely manual process of sewer rehabilitation design leaves a number of unanswered questions, such as: (1) Does the solution offer the greatest structural benefit to the network? (2) Is the solution the most cost-effective solution available? (3) Does the solution most greatly reduce the risk of critical asset failure? The application of a multi-objective genetic algorithm optimisation model, coupled with an enhanced critical risk methodology, has successfully answered these questions when applied to a case study data set provided by South West Water (UK)

Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

“Offsetting” Crisis? - Climate Change Cap-and-Trade Need Not Contribute to Another Financial Meltdown

In 2009, the promise of a comprehensive federal cap and trade bill to address climate change fell apart. At least in part, this was due to the fears that exotic \u27carbon\u27 financial instruments might cause more financial crises. As California launches it economy wide carbon trading system, and other regional systems and the even possibly the EPA consider cap and trade, it is important to revisit what, if anything, about carbon denominated financial instruments might lead to financial market problems. The most problematic of the instruments, offsets, can be designed to lessen financial risk from underlying asset failure

Modelling and flow conditioning to manage discolouration in trunk mains

This paper presents predictive discolouration modelling and subsequent field trial results for a cast iron trunk main network. This enabled a UK water company to propose an ‘operational flow conditioning’ maintenance plan that reduces discolouration risk, improves network resilience and asset condition and yet does not require the trunk main to be decommissioned for invasive cleaning. This represents substantial time and cost benefits. Pre-and-post trial turbidity monitoring data is also presented which identified a daily flux of material, a factor in the regeneration of material layers that have been shown to cause discolouration when mobilised. Additional data detecting the occurrence of pressure transients is also presented, a possible cause of contaminant ingress and asset failure

Travel Behaviour Response to Major Transport System Disruptions: Implications for Smarter Resilience Planning

No abstract available

Systemic risk in dynamical networks with stochastic failure criterion

Complex non-linear interactions between banks and assets we model by two time-dependent Erd\H{o}s Renyi network models where each node, representing bank, can invest either to a single asset (model I) or multiple assets (model II). We use dynamical network approach to evaluate the collective financial failure---systemic risk---quantified by the fraction of active nodes. The systemic risk can be calculated over any future time period, divided on sub-periods, where within each sub-period banks may contiguously fail due to links to either (i) assets or (ii) other banks, controlled by two parameters, probability of internal failure $p$ and threshold $T_h$ ("solvency" parameter). The systemic risk non-linearly increases with $p$ and decreases with average network degree faster when all assets are equally distributed across banks than if assets are randomly distributed. The more inactive banks each bank can sustain (smaller $T_h$), the smaller the systemic risk---for some $T_h$ values in I we report a discontinuity in systemic risk. When contiguous spreading becomes stochastic (ii) controlled by probability $p_2$---a condition for the bank to be solvent (active) is stochastic---the systemic risk decreases with decreasing $p_2$. We analyse asset allocation for the U.S. banks.Comment: 7 pages, 7 figure