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Why use a strong observation and coaching process? In today’s high-risk rail industry, the demanding nature of the work, the fast pace, and resource constraints leads to rail workers adapting in unforeseen ways often reducing established safety margins.

Observations and the associated leadership presence in the work environment are needed to ensure high, uncompromising standards are being maintained every day. Regular observations provide management with insights into the current work culture (behaviors) while also identifying systems and processes that may need improvement.

The purpose of this presentation is to present proven methodologies that will enhance your organization’s employee engagement. Without engagement, improvement for individual and organizational performance is at considerable risk of failure. Consider that any time an organization strives to improve performance, it must ask people to change their behavior. Desired behavior is the only way a business can accomplish and sustain any new initiative. When seeking performance improvements, it’s essential to recognize that behavioral change is necessary. Effective observation and coaching present a valuable opportunity to leverage feedback, yielding immediate behavior changes that align with your organization’s safety expectations for personnel, assets, processes, and the environment. Additionally, this process fosters high-quality work, improved morale, enhanced ownership, and greater productivity when executed correctly. To facilitate effective learning, this presentation will leverage cutting-edge technology in adult learning, specifically video-based training (VBT). By harnessing the power of visual immersive learning, VBT offers a faster and more comprehensive learning experience, enabling efficient and accurate coverage of presentation material.

The presentation will conclude with actionable solutions that stakeholders can readily implement, to enhance observation and feedback in rail operations and foster a culture of safety throughout the industry.

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The MTR Corporation is committed to constantly seeking ways to improve safety and efficiency in our railway network. Given the size of the network, there are numerous maintenance tasks that must be performed to ensure safety. However, with over 19 hours of operation each day, the maintenance window is limited. To address this challenge, MTR seeks to leverage advancements in technology to enhance maintenance efficiency, prioritize maintenance tasks, and safeguard operational safety through smart maintenance. This paper proposes an innovative approach to enhancing railway safety through the use of big data analytics and AI-powered for smart maintenance.

The paper aims to explore the potential of these technologies to improve safety in the permanent way. By leveraging big data analytics, potential defects or areas of high risk can be identified, while artificial intelligence can predict potential failures before they occur, allowing for predictive maintenance and reducing downtime. With the increasing demand for faster and more efficient services, railway companies are facing challenges in ensuring safety and reliability. One of the critical areas that need constant attention is the state of the railway infrastructure, including the track, crossing, and other components of the permanent way. To address these challenges, MTR has adopted big data analytics and artificial
intelligence (AI) to enhance railway safety. Big data analytics can provide valuable insights into the state of the railway infrastructure, enabling the identification of potential defects or areas of high risk. By analyzing massive amounts of data generated by railway operations, patterns and trends in track performance can be identified, leading to a more proactive approach to maintenance. For instance, by analyzing sensor data from trains and tracks, potential weaknesses and defects can be detected before they escalate into serious safety hazards.

In conclusion, the paper highlights the importance of embracing new technologies to enhance railway safety and efficiency. The proposed approach of using big data analytics and artificial intelligence for smart maintenance has significant potential to transform the way railway safety is managed and improve the overall performance of the railway system.

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Presentation

cape-town

Presentation

cape-town

Presentation

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Rail transport, being a global mode of transportation, effectively facilitates long-distance travel for passengers worldwide, benefiting travelers from all corners of the globe. However, derailment poses a significant challenge to the industry’s safety and reliability. Regular inspection of tracks help to identify any signs of wear, damage, or defects that could lead to derailments. These inspections involve examining track components such as rails, joints, switches, and fastenings, for any abnormalities or potential hazards. Inadequate ightening or loosening of nuts and bolts can occur due to vibrations, temperature variations, or inadequate maintenance practices. Loose or missing fasteners can cause track misalignment and instability, potentially leading to derailments.

The main objective of this work is to detect various components of joint bars including nuts, bolt heads, and identifying specific cases such as missing nuts and holes. This study examines the latest advancements in computer vision integrated into You Only Look Once version 5 (YOLOv5), a state-of-the-art object detection framework. To achieve this, a model based on YOLOv5 is constructed using input images captured by drone, which provide high-resolution images from a bird’s eye view. The use of drones offers significant advantages over traditional inspection methods such as improved safety, cost-effectiveness, and the ability to cover large areas quickly. The model is then trained using the captured images, and its accuracy is subsequently evaluated. Once the rail components were identified and consolidated, further data integration and analysis took place, resulting in a significant enhancement of the overall accuracy to 97 percent. The results show how good the system is at recognizing problems and their components accurately.

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Non-technical skills (NTS) are “the cognitive, social, and personal resource skills that complement technical skills, and contribute to safe and efficient task performance” and have been investigated in a range of high-risk industries (Flin, 2008). Research by RSSB produced an NTS framework and an accompanying set of behavioural markers for the train driver role (RSSB, 2012). However, a rail industry consultation found that NTS integration is varied across organisations and across rail safety-critical roles, partly due to a limited understanding of what NTS look like in non-driver roles (RSSB, 2022). The objectives of the research presented was to identify and classify NTS behaviours and strategies for rail controllers and rolling stock maintenance staff, and to develop humancentred NTS materials to support the observation, development and measurement of NTS in these roles. Wider factors influencing human performance in these roles were also identified and will be discussed.

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Train-pedestrian collisions are the most common accident type among all fatal railway accidents occurring on Finnish railways. Specifically, around 50 to 60 people are killed in such collisions every year, and this number has stayed rather stable in recent years. The main aim of this presentation is to describe the frequency of fatalities, timing of collisions and characteristics of persons killed in train-pedestrian collisions on Finnish railways during 2005Ð2020. In addition, similarities and differences between different types of events (accidents vs. suicides) were investigated to support the identification of focal areas for future research, and to help determine general preventative strategies for train-pedestrian collisions and/or separate strategies for different types of events.

The data for the analysis was obtained from five different sources. The data covered 889 train-pedestrian fatalities, including 795 suicides, 73 trespassing fatalities and 21 unclassified events. The characteristics included in our analysis covered gender and age, pre-crash behaviour, intoxication, mental health, whether there had been a past suicide attempt, whether a suicide note or farewell had been left and the type of train involved. The results showed that most victims were male for accidents and suicides. It was also found that most suicide victims waited on the tracks before the train arrived. Less frequent behaviours included running or jumping in front of the train and walking in front of the train. Accidents happened most frequently in situations where a person was lying/sitting on the tracks or was crossing the track. Both suicides and accidents had a relatively even distribution by month and weekday.

Multivariate logistic regression analyses were used to assess the impact of various background variables on i) whether a collision had been accidental or intentional, and ii) whether a track kilometre was associated with one or multiple train pedestrian collisions. The results showed that a collision was significantly more likely to be accidental if it occurred on a weekend, if the victim was intoxicated by alcohol, medicine and/or drugs and if travelling in a group.

The results of both analyses will be presented and discussed during the presentation. The results of our analyses show the main demographic groups and the type of behaviour that should be focused on in preventative efforts. One of our main findings is that the effective prevention of train-pedestrian collisions calls for a systems approach involving effective measures introduced by several authorities covering urban planning, railways, education and public health.

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PRASA mandate is to transport passengers from communities to work throughout the City and industrial areas. It is very important for the trains to be on time at all times to keep and attract more customers to use the railway mode of transport. This would assist in improving the economy of the country since the Western Cape railway lines interconnect all the communities to areas of work opportunities and it is relatively affordable when compared to other modes of transport. To have a reliable traction network, the system has to have proper maintenance processes to prevent potential failures. In this research, different types of potential failures in the system will be analyzed and ranked according to their impact on the system i.e. how they affect the running of trains and the faults per corridor/section will be categorized. Considering the substations and overhead track equipment, the major faults that heavily affect the trains are pantograph hook ups and catenary burn-ups as opposed to substation faults since the substations are mostly double unit substations. Potential root causes for failures would be analyzed and suitable solutions would be proposed for implementation. The data management information system (MIS) would be used for analyzing the fault history of the network and data loggers will be used to monitor different parameters at the substations and on the load side for analysis. The research would be looking at the repair crewÕs response time to faults, the time taken to repair each fault and the time recorded between faults. These indicators would be used to determine the reliability of the network and the probability of failures per section. An extension of the existing 33 kV AC network would be investigated for possible reliability improvement.

This paper would also consider the impact of the power quality of the network on the reliability of the equipment in the system. Measurements would be taken at the substations and on the load (train) side and compared. This would show the impact of the load on the substations or vice versa and it would also show the losses on the overhead wires. The measured data would assist in understanding the network behavior i.e. the impact of wave filters on the DC output as well as the impact of track switches on power quality. Fault path indicators would be used to monitor faults and quickly isolate to reduce power failures and repair time. Energy meters are also used for the monitoring of faults to effectively improve reliability.

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Governments and purchasers of new and refurbished Rolling Stock are continuously seeking to increase the percentage of spend on material for these projects from local suppliers to boost the local economy, create jobs and sustain the rail manufacturing industry in their country. The initiative is understood and supported by major rail manufactures however besides the economic constraints (availability of materials, equipment, skills and economies of scale to justify the investment), rolling stock manufactures need to still maintain the safety and integrity of the products that are manufactured irrespective of the local content.

In addition to increasing the percentage of spend on local suppliers, cost improvements are also perused through innovative design ideas by technical and engineering teams. As for localisation, these improvements also challenges the product Safety team, tasked to ensure that the safety and integrity of the design is at least maintained, to understand and review all aspects of the proposed change.

This paper provides an overview of the safety related processes and systems approach applied by Gibela when executing localization projects and when optimising the design. Changes and proposed changes made to the rail technology and also challenges encountered are given with practical examples. The examples include mechanical and electrical topics. The key aspect of this paper can be summarised as Ôreshaping railways in an uncertain worldÕ without compromising Safety Integrity. In doing this local suppliers are challenged with, amongst others, applying international requirements, leading to an increase in knowledge, skills and quality which is needed to produce safe products. Gibela is challenged to fully understand each change, being localisation or optimisation, the associated risk if any, and to then apply the Safety Design Process