Causal Modelling of Lower Consequence Rail Safety Incidents

Waiting for copyright information from publisherThe Safety Risk Model (SRM) is a key source of information for the GB rail industry. It is a structured representation of the 120 hazardous events that can lead to injury or death during the operation of the railway and is used to estimate the risk to passengers, workers and third parties. The SRM includes both rare but high consequence events such as train collisions and more frequent but lower consequence events such as passenger accidents at stations. In aggregate, these lower consequence events make an important contribution to the overall risk, which is measured by a weighted sum of injuries of different severity. Where possible, the SRM is derived from historical incident data, but the derivation of the model parameters still present challenges, which differ for different subsets of events. High consequence events occur rarely so it is necessary to use expert judgement in detailed models of these incidents. In comparison, the low consequence events occur more frequently, but both records of incidents and the models in the SRM are less detailed. The frequency of these low consequence events is sufficient to allow both the absolute risk and trends in the overall risk to be monitored directly. However, without explicit causal factors in the data or the model, the models are less able to support risk management directly, since this requires estimates of the risk reduction possible from particular interventions and control measures. Moreover, such estimates must be made locally, taking account of the local conditions, and at each location even the low consequence events are infrequent. In this paper we describe an approach to modelling the causes of low consequence events in a way that supports the management of risk. We show both how to extract more information from the available data and how to make use of expert judgement about contributory factors. Our approach uses Bayesian networks: we argue their advantages over fault and event trees for modelling incidents that have many contributory causes. Finally, we show how the new approach improves safety management, both by estimating the contribution of the underlying causes to this risk and by predicting how possible management interventions and control measures would reduce this risk

Condition monitoring approach for permanent magnet synchronous motor drives based on the INFORM method

This paper proposes a monitoring scheme based on saliency tracking to assess the health condition of PMSM drives operating under non stationary conditions. The evaluated scheme is based on the INFORM methodology, which is associated to the accurate sensorless control of PM drives without zero speed limitation. The result is a monitoring scheme that is able to detect faults that would be very difficult to evaluate under nonstationary conditions. A relevant aspect of the proposed scheme is that it remains valid for full speed range, and can be used for standstill operation. Additionally, the approach is insensitive to the inverter nonlinearities which enhance the detection capabilities further respect to similar topologies. In this work the proposed approach is evaluated numerically and experimentally in the presence of incipient winding faults and inter-turn short circuits in a PM conventional drive. The obtained results show quick response and excellent detection capabilities not only in the detection of faults, but to determine their magnitude which is vital to avoid further degradation

The role of hardness on condition monitoring and lifing for high temperature power plant structural risk management

In this work, the use of hardness data in a novel predictive lifing model is explored. This study provides for the first time large amounts of site hardness data acquired during successive outages on an ageing coal fired power plant and draws conclusions regarding interpretation of these data in accordance with current practice, which is included in a case study. A novel, phenomenological relationship between room temperature hardness and creep data, obtained by uniaxial creep and impression creep tests, has been found and used for an innovative lifing approach that includes hardness data in a creep damage model. The latter is discussed with a description of how it could be practically implemented and validated in-service

A simulation study of atmospheric turbulence over the southern coast of Long Island during on-shore flow

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CRACUK model description Appendices to the CRAC2 model description

SIGLEAvailable from British Library Document Supply Centre- DSC:9091.9F(SRD-R--359) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

A modelling study of dispersion of elevated plumes at a coastal location during onshore flow

4.00Available from British Library Document Supply Centre- DSC:9091.9F(SRD-R--307) / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo