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

Learning from accidents : machine learning for safety at railway stations

In railway systems, station safety is a critical aspect of the overall structure, and yet, accidents at stations still occur. It is time to learn from these errors and improve conventional methods by utilizing the latest technology, such as machine learning (ML), to analyse accidents and enhance safety systems. ML has been employed in many fields, including engineering systems, and it interacts with us throughout our daily lives. Thus, we must consider the available technology in general and ML in particular in the context of safety in the railway industry. This paper explores the employment of the decision tree (DT) method in safety classification and the analysis of accidents at railway stations to predict the traits of passengers affected by accidents. The critical contribution of this study is the presentation of ML and an explanation of how this technique is applied for ensuring safety, utilizing automated processes, and gaining benefits from this powerful technology. To apply and explore this method, a case study has been selected that focuses on the fatalities caused by accidents at railway stations. An analysis of some of these fatal accidents as reported by the Rail Safety and Standards Board (RSSB) is performed and presented in this paper to provide a broader summary of the application of supervised ML for improving safety at railway stations. Finally, this research shows the vast potential of the innovative application of ML in safety analysis for the railway industry

Reference values for railway sidings track geometry

Railway sidings are operated at speeds much lower than those used on national railway lines; a typical speed is 6 km/h. In establishing reference values for maintenance of railway infrastructure in terms of the geometry for such operating conditions, it is noted that both national and European regulations do not provide specific information regarding railway sidings. The overall objective of the research is, therefore, the definition of possible reference values for track geometry, based on those adopted by European rail networks (European and national standards), which can guarantee the appropriate security level for low speed operation typical of railway sidings connected to the national network. The basic principle in defining these values is the maximization of technical-economic efficiency and the maintenance of the acceptability of the risk associated to railway operation. The research results can therefore provide useful information about the cost-effective management of maintenance and safe operation for railway sidings. For this purpose, the approach was inspired to that of Regulation 402/2013, which defines at European level a common safety method for risk analysis. Quantification of probabilities and damages should be based on simulation models because the available statistics do not allow significant results to be inferred. However, the research sector has not yet produced a consolidated modelling. For these reasons, and since it is not possible to quantify probabilities reliably, the proposals resulting from this research are based on the identification of situations where it can be shown that the hazard probability remains unchanged. The approach used to formulate possible reference values valid outside of national networks (railway sidings) is based on an understanding of the underlying principles of the codes of good practice, on the formulation of hypotheses conform to the same principles, and the proposals about mitigative measures of risk associated to the use of different reference values, such as to keep the risk of the railway within the limits of acceptability, acting conservatively so as to keep unchanged, or reduce, the probability of hazardous events.The assessment parameters, object of the first phase of the research referred to in this work and used here as an example, are longitudinal level and alignment of railway track. In the case of vehicles running at low speed, the study was conducted by varying the magnitude of the reference values by using values that belong to external intervals with respect to those in accordance with European and national codes of good practice, examining the corresponding effects on the physical quantities related to safety. The effects of their variations on the wheel-rail interaction forces were studied using a simple dynamic model (with one degree of freedom) and a random generated excitation given by track defectiveness and the corresponding random response in terms of vertical and lateral contact forces (Q and Y)

Introducing the STAMP method in road tunnel safety assessment

After the tremendous accidents in European road tunnels over the past decade, many risk assessment methods have been proposed worldwide, most of them based on Quantitative Risk Assessment (QRA). Although QRAs are helpful to address physical aspects and facilities of tunnels, current approaches in the road tunnel field have limitations to model organizational aspects, software behavior and the adaptation of the tunnel system over time. This paper reviews the aforementioned limitations and highlights the need to enhance the safety assessment process of these critical infrastructures with a complementary approach that links the organizational factors to the operational and technical issues, analyze software behavior and models the dynamics of the tunnel system. To achieve this objective, this paper examines the scope for introducing a safety assessment method which is based on the systems thinking paradigm and draws upon the STAMP model. The method proposed is demonstrated through a case study of a tunnel ventilation system and the results show that it has the potential to identify scenarios that encompass both the technical system and the organizational structure. However, since the method does not provide quantitative estimations of risk, it is recommended to be used as a complementary approach to the traditional risk assessments rather than as an alternative. (C) 2012 Elsevier Ltd. All rights reserved

Analyzing and Predicting Railway Operational Accidents Based on Fishbone Diagram and Bayesian Networks

The prevention of railway operational accidents has become one of the leading issues in railway safety. Identifying the impact factors which significantly affect railway operating is critical for decreasing the occurrence of railway accidents. In this study, 8440 samples of accident data are selected as the datasets for analyzing. Fishbone diagram is applied to obtain the factors which cause the accident from the perspective of human-equipment-environment-management system theory. Then, the Bayesian network method was selected to establish a railway operation safety accident prediction model, and the sensitivity analysis method was used to obtain the sensitivity of each variable factor to the accident level. The results show that season, location, trouble maker and job function have a significant impact on railway safety, and their sensitivity was 0.4577, 0.4116, 0.3478 and 0.3192, respectively. Research helps the railway sector to understand the fundamental causes of accidents, and provides an effective reference for accident prevention, which is conducive to the long-term development of railway transportation

Maximum risk reduction with a fixed budget in the railway industry

Decision-makers in safety-critical industries such as the railways are frequently faced with the complexity of selecting technological, procedural and operational solutions to minimise staff, passengers and third parties’ safety risks. In reality, the options for maximising risk reduction are limited by time and budget constraints as well as performance objectives. Maximising risk reduction is particularly necessary in the times of economic recession where critical services such as those on the UK rail network are not immune to budget cuts. This dilemma is further complicated by statutory frameworks stipulating ‘suitable and sufficient’ risk assessments and constraints such as ‘as low as reasonably practicable’. These significantly influence risk reduction option selection and influence their effective implementation. This thesis provides extensive research in this area and highlights the limitations of widely applied practices. These practices have limited significance on fundamental engineering principles and become impracticable when a constraint such as a fixed budget is applied – this is the current reality of UK rail network operations and risk management. This thesis identifies three main areas of weaknesses to achieving the desired objectives with current risk reduction methods as: Inaccurate, and unclear problem definition; Option evaluation and selection removed from implementation subsequently resulting in misrepresentation of risks and costs; Use of concepts and methods that are not based on fundamental engineering principles, not verifiable and with resultant sub-optimal solutions. Although not solely intended for a single industrial sector, this thesis focuses on guiding the railway risk decision-maker by providing clear categorisation of measures used on railways for risk reduction. This thesis establishes a novel understanding of risk reduction measures’ application limitations and respective strengths. This is achieved by applying ‘key generic engineering principles’ to measures employed for risk reduction. A comprehensive study of their preventive and protective capability in different configurations is presented. Subsequently, the fundamental understanding of risk reduction measures and their railway applications, the ‘cost-of-failure’ (CoF), ‘risk reduction readiness’ (RRR), ‘design-operationalprocedural-technical’ (DOPT) concepts are developed for rational and cost-effective risk reduction. These concepts are shown to be particularly relevant to cases where blind applications of economic and mathematical theories are misleading and detrimental to engineering risk management. The case for successfully implementing this framework for maximum risk reduction within a fixed budget is further strengthened by applying, for the first time in railway risk reduction applications, the dynamic programming technique based on practical railway examples

Are Britain’s railways costing too much? Perspectives based on TFP comparisons with British Rail: 1963-2002.

Following the Hatfield accident in October 2000, the cost of running Britain’s railways has increased very sharply, leading to considerable debate about whether current cost levels are reasonable. This paper seeks to inform this debate by assessing post-Hatfield cost and TFP levels (2000/01 to 2001/02) against the historical precedents set by British Rail and the early experience of the newly-privatised industry (1963 to 1999/00). The results show that industry cash costs rose by 47% between 1999/00, the last financial year before Hatfield, and 2001/02 - but, surprisingly, with train operating costs (TOCs and freight operators) accounting for 42% of this growth. The results also show that the post-Hatfield cost spike is unprecedented when compared against historical benchmarks, indicating that recent cost rises cannot simply be explained by the investment cycle or so-called “bow-wave” effects. Furthermore, according to the preferred models, post-Hatfield productivity levels are lower than at any time over the last four decades. Analysis of long-term data on quality and safety measures indicates that an excessive focus on rail safety may offer part of the explanation for the recent cost growth, with the emphasis on safety also resulting in less attention to punctuality and reliability

Application of Cognitive Systems Engineering Approach to Railway Systems (System for Investigation of Railway Interfaces)

This chapter presents the results of a cognitive systems engineering approach applied to railway systems. This application is through the methodology of ’System for Investigation of Railway Interfaces – SIRI’. The utility of the chapter lies in highlighting errors in the current approaches to safety risk management