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

Bayesian Network Models for Making Maintenance Decisions from Data and Expert Judgment

To maximize asset reliability cost-effectively, maintenance should be scheduled based on the likely deterioration of an asset. A number of types of statistical model have been proposed for predicting this but they have important practical limitations. We present a Bayesian network model that can be used for maintenance decision support to overcome these limitations. The model extends an existing statistical model of asset deterioration, but shows how i) failure data from related groups of asset can be combined, ii) data on the condition of assets available from their periodic inspection can be used iii) expert knowledge of the causes deterioration can be combined with statistical data to adjust predictions and iv) the uncertain effects of maintenance actions can be modelled. We show how the model could be used for a range of decision problems, given typical data likely to be available in practice

Engineering adaptive user interfaces using monitoring-oriented programming

User interfaces which adapt based on usage patterns, for example based on frequency of use of certain features, have been proposed as a means of limiting the complexity of the user interface without specialising it unnecessarily to particular user profiles. However, from a software engineering perspective, adaptive user interfaces pose a challenge in code structuring, and separation of the different layers of user interface and application state and logic can introduce interdependencies which make software development and maintenance more challenging. In this paper we explore the use of monitoring-oriented programming to add adaptive features to user interfaces, an approach which has been touted as a means of separating certain layers of logic from the main system. We evaluate the approach both using standard software engineering measures and also through a user acceptance experiment - by having a number of developers use the proposed approach to add adaptation logic to an existing application.peer-reviewe

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

Evaluation of saliency tracking as an alternative for health monitoring in PMSM-drives under nonstationary conditions

This paper evaluates the capability of saliency tracking to assess the health condition of permanent magnet synchronous motor (PMSM) drives operating under nonstationary conditions. The evaluated scheme is based on saliency tracking methods, which are associated to the accurate sensorless control of AC drives without zero speed limitations. In this work two representative saliency tracking architectures are evaluated: High Frequency (HF) injection, and PWM transient excitation. Although a monitoring approach based on HF injection was previously reported, a comparative study to evaluate the most representative saliency tracking schemes to assess health condition in drives was still missing. The aim of this work is to fill out this gap by evaluating and comparing two saliency-based monitoring schemes (one based on HF-injection and the other based on PWM transient excitation) to evaluate their performance in the presence of inter-turn winding faults. Simulation and experimental results are presented which confirm that both schemes offer excellent detection capabilities and that are suitable for drives operating under nonstationary conditions including standstill operation. Significant differences are also found for instance, PWM transient excitation offers improved accuracy since the approach is not affected by the inverter nonlinearities and is suitable for full-speed range applications. The main drawback here is complexity and the hardware requirements. Schemes based on HF-injection proved to be very simple and provide comparable results; however a good performance is only guaranteed for the zero-to-medium speed range applications which limit their applicability

Fault Indicators of Partial Discharges in Medium-Voltage Systems

Abstract. The safety and reliability of the electricity system, these are the two basic keywords present in the energy sector. One of the ways to improve the overall security of the electricity system is to reduce the failure rate of transmission lines. In medium-voltage distribution systems, this predominantly concerns overhead lines and cable lines. This paper discusses options on how to decrease the failure rate of medium-voltage overhead lines with covered conductors

Towards A Model-Based Asset Deterioration Framework Represented by Probabilistic Relational Models

Most asset deterioration tools are designed for a specific application, as a consequence, a small change of the specification may result in a complete change of the tool. Inspired by the model-based approach of separating problem specification from analysis technique, we propose a model-based asset deterioration assessment framework using probabilistic relational models. The probabilistic relational models express abstract probabilistic dependency covers a range of deterioration modelling assumptions. An expert in the domain of asset deterioration can then use his knowledge of the factors that affect deterioration to customise the abstract models to a specific application, without requiring a detailed understanding the underlying computational framework. We illustrate the use of the framework with multiple variants of deterioration models

Review of HRP Positions

The Department of Energy (DOE) Human Reliability Program (HRP), published as 10 CFR Part 712, is currently being reviewed and revised to address concerns identified during its implementation. Although these ''page changes'' primarily incorporate clarification of terms and language, the following discussion relates to broadening the definition of positions that require HRP certification that is found in {section}712.10