Human error in the design of a safety-critical system

From the introduction:This thesis is an investigation into some o f the causes and possible remedies to the problem of human error in a complex human-machine system. The system in question is engaged in the design of computer software for the control of railway signalling infrastructure. Error in its operation has the potential to be lethally destructive, a fact that provides not only the system’s epithet but also the primary motivation and significance for its investigation

Research on Fault Diagnosis of ZPW-2000K Track Circuit Based on RS-BN Algorithm

For problem of complex fault types and uncertain diagnostic features of the ZPW-2000K track circuit, traditional fault diagnosis mainly adopts manual diagnosis methods, which leads to low automatic diagnosis. This paper proposes a fault diagnosis method based on Rough Sets (RS) reduction model and Bayesian Network (BN) structure learning fusion. Firstly, data mining and feature extraction are performed on the fault data table, and expert knowledge is built into the prior knowledge base. Secondly, the K2 algorithm is used to train the fault feature quantity, and the BN model is built by combining the prior knowledge base. Then, a diagnostic decision table is established through the fault instance, and RS is used for attribute reduction, dimensionality reduction, and simplified model. The MLE algorithm is used again to learn the parameters to obtain the conditional probability table of the model, and the complete BN structure is established based on the RS-BN algorithm. Finally, the comparative analysis of the simplified model and the non-simplified model is carried out. Through the experimental simulation of the ZPW-2000K track circuit fault of a high-speed railway station, the accuracy and effectiveness of the diagnostic method are verified

A framework to support human factors of automation in railway intelligent infrastructure

Technological and organisational advances have increased the potential for remote access and proactive monitoring of the infrastructure in various domains and sectors – water and sewage, oil and gas and transport. Intelligent Infrastructure (II) is an architecture that potentially enables the generation of timely and relevant information about the state of any type of infrastructure asset, providing a basis for reliable decision-making. This paper reports an exploratory study to understand the concepts and human factors associated with II in the railway, largely drawing from structured interviews with key industry decision-makers and attachment to pilot projects. Outputs from the study include a data-processing framework defining the key human factors at different levels of the data structure within a railway II system and a system-level representation. The framework and other study findings will form a basis for human factors contributions to systems design elements such as information interfaces and role specifications

Online rescheduling for mass rapid transit system using evolutionary techniques with fuzzy aggregation of multiple objectives

Master'sMASTER OF ENGINEERIN

A literature review of Artificial Intelligence applications in railway systems

Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub-domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges

Human factors of future rail intelligent infrastructure

The introduction of highly reliable sensors and remote condition monitoring equipment will change the form and functionality of maintenance and engineering systems within many infrastructure sectors. Process, transport and infrastructure companies are increasingly looking to intelligent infrastructure to increase reliability and decrease costs in the future, but such systems will present many new (and some old) human factor challenges. As the first substantial piece of human factors work examining future railway intelligent infrastructure, this thesis has an overall goal to establish a human factors knowledge base regarding intelligent infrastructure systems, as used in tomorrow’s railway but also in many other sectors and industries. An in-depth interview study with senior railway specialists involved with intelligent infrastructure allowed the development and verification of a framework which explains the functions, activities and data processing stages involved. The framework includes a consideration of future roles and activities involved with intelligent infrastructure, their sequence and the most relevant human factor issues associated with them, especially the provision of the right information in the right quantity and form to the right people. In a substantial fieldwork study, a combination of qualitative and quantitative methods was employed to facilitate an understanding of alarm handling and fault finding in railway electrical control and maintenance control domains. These functions had been previously determined to be of immediate relevance to work systems in the future intelligent infrastructure. Participants in these studies were real railway operators as it was important to capture users’ cognition in their work settings. Methods used included direct observation, debriefs and retrospective protocols and knowledge elicitation. Analyses of alarm handling and fault finding within real-life work settings facilitated a comprehensive understanding of the use of artefacts, alarm and fault initiated activities, along with sources of difficulty and coping strategies in these complex work settings. The main source of difficulty was found to be information deficiency (excessive or insufficient information). Each role requires different levels and amounts of information, a key to good design of future intelligent infrastructure. The findings from the field studies led to hypotheses about the impact of presenting various levels of information on the performance of operators for different stages of alarm handling. A laboratory study subsequently confirmed these hypotheses. The research findings have led to the development of guidance for developers and the rail industry to create a more effective railway intelligent infrastructure system and have also enhanced human factors understanding of alarm handling activities in electrical control

Intelligent Systems Supporting the Use of Energy Systems and Other Complex Technical Objects, Modeling, Testing and Analysis of Their Reliability in the Operation Process

The book focuses on a novel application of Intelligent Systems for supporting the operation and maintenance of power systems or other technical facilities within wind farms. Indicating a different perception of the reliability of wind farm facilities led to the possibility of extending the operation lifetime and operational readiness of wind farm equipment. Additionally, the presented approach provides a basis for extending its application to the testing and analysis of other technical facilities