Use of Scenarios, Barrier Parameters and process Indicators to Analyse Freight Train Derailments

  • Xing Peng

Student thesis: Doctoral Thesis


The main aim of this work is to analyze the causes of freight train derailments using the scenario method, barrier description, and process indicators. This work utilizes systemic theories to analyze freight train derailments over the last ten years. Firstly, the work revealed that track deterioration, ineffective inspection, and repair actions were all contributory factors to the derailments. The safety management system's deficiencies, such as the inspections within the risk control procedure, were not always consistent or reliably achieved simply by compliance with the standard; the work deficiencies refer to the control and constraint of the risk at each management level.
Secondly, the work used scenario analysis and barrier description to establish the causal reasons for the accidents, for example, the Porthkerry derailment. The accident investigation concluded that the track management inspection or repair procedure for the track deterioration, such as the deficiencies of track management at the Newcastle track site (Heworth derailment) and a few parts of the switches and crossing locations. Based on the RAIB original reports, the author extracts the Functional Barrier model to improve the systemic reliability evaluation.
Thirdly, the ORR Safety Risk Strategy (ORR, 2019) was reviewed. This states that the reliability of control for the track irregularity quality, such as, the reliance on human judgment and intervention to implement the detection is inadequate. It is necessary to increases the reliance on monitoring and reviewing activity to provide essential risk control. Then the author applied the Human cognitive study to compare failure probabilities and the threshold levels in each Barrier Model.
Furthermore to improve the control of the risks, the reliablility of the inspection of critical assets is improving through the use of automated inspection arrangements (Plain line Pattern recognition method). Sustainable simulation methods and follow-up inspection repairs are used to examine the interaction between track and freight vehicles. Moreover, the identification of the S&C system was a priority risk area due to its complexity, potential failure modes, and reliance on individuals to control risk.
In addition, investigations are undertaken to assess the different derailment mechanisms, for instance, the track geometry deterioration, the track twist, the degradation at switches and crossing, the track void, the vehicle frame twist, the suspension characteristics, the friction liner performance. These have been considered in the developed risk models.

The key points and contributions of this thesis are listed below:
A) The qualitative and quantitative analysis of the barrier model
B) The comparison between the HEART and HFACS method
C) The uneven loading derailment due to the standard intervention
D) The reliability analysis of the railway components
E) Scenario analysis

In summary, the work discussed and compared the main safety theories and models, such as the utilization of the Swiss Cheese Model, Human Factor Analysis and Classification System (HFACS), Systems Theoretic Accident Modeling and Processes model (STAMP); and define the capability and advantage of each application in the various accident analysis procedures.
The thesis developed a new methodology for the derailment failure mechanism, cognitive reliability model, and asymmetric loading issue; the scenario analysis is based on the understanding of the SRK framework to improve the integrated safety performance. The investigation of S&C track geometry established the increased likelihood of deficiencies in conditions of the safety system. This work also demonstrated the holistic map of the poor performance between wheel and vehicle and the overall track fault. The work concluded with the quantitative analysis based on the state transition model (track geometry recording data), and the qualitative analysis to demonstrate HEART and cognitive reliability understanding of the performance shaping factors/error producing a condition in the normal vehicle/track maintenance regime.
Date of Award16 Dec 2022
Original languageEnglish
SupervisorSimon Iwnicki (Main Supervisor) & Rawia El Rashidy (Co-Supervisor)

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