Simulação Empolamento de Via track Buckling em Ansys

Table of Contents 1 Introduction 4 11 Background 4 12 Problem Statement 5 121 Misalignments 5 122 Preventing Misalignment 6 13 Project Aims 6 14 Project Objectives 6 15 Research Questions 6 16 Outline of Dissertation 7 2 Critical Literature Review 7 21 Introduction 7 22 Relevant Theories Formulas and Prior Knowledge 7 221 Prior Knowledge 8 222 Rail Critical Temperature 8 223 Formulas Modelling of Thermal Environment around Rail 8 224 Speed Restriction associated with temperature 9 225 FRA Model Outputs 10 226 Heat Transfer Equation 10 227 Temperature Monitoring Devices 11 23 Industry Practice Codes and Standards 12 24 Case Studies 12 241 Aurizon Case Study 12 25 Research Gap and Potential impact Significances 13 251 Identifying specific Research Gaps 13 26 Hypothesis 13 27 Summary of Literature Review 15 3 Methodology 16 31 Introduction 16 32 Explore Research Methodological Approaches 16 33 Identify Select and Justify Research Methodological Approach 16 34 Outline the Details of the Methodology 16 341 AudioFrequency Analysis Method 17 342 Track Geometry Measurement Systems 17 343 Temperature Monitoring Devices 17 35 Methods Processes Software and Tools 18 36 Expected Outcomes 18 37 Limitations of the Study 18 38 Timeline and Project Management 19 4 Results and Analysis 20 5 Benefits Risks and Consequences 30 6 Conclusion 31 7 References 32 Introduction Project Title Track Inspection Automated Systems Solutions in the CRN or Asset Management Strategies in railway to tackle Temperature inscrease in NSW Country Regional Network CRN Background During the summer of 20192020 the rail industry experienced several impacts In New South Wales bushfires closed multiple major train lines including the Main Western Line through the Blue Mountains the Southern Highlands Line between Goulburn and Macarthur and the Unanderra Line between Moss Vale and Unanderra Pearce 2020 Predicting rail temperature is crucial for developing effective rail management plans to prevent derailments due to track buckling Rail temperature a key factor in track stability is influenced by rising air temperatures due to global warming Additionally the widespread use of continuous welded rails CWRs in railway systems enhances the riding experience by reducing train vibration and noise However these CWRs are more susceptible to buckling Figure 1 making temperature prediction even more essential Pangjo Chun1 2021 Figure 1 Track Buckles in the summer ENSCO Rai Given the increasing challenges of climate change especially the effects of rising temperatures on infrastructure stability the urgency for advanced and efficient rail inspection methods in New South Wales Country Regional Network CRN is critical In NSW the mean temperature for 20112020 was about 11C higher than late last century 196190 with 2018 and 2019 being the warmest years on record Mean temperatures during 2020 in NSW were generally above average except for the states southwest SOE 2021 Furthermore climate projections indicate that these temperature trends will continue potentially leading to more frequent and severe impacts on rail infrastructure stability bom 2020 Given the escalating temperatures and their adverse effects on the lateral stability of railway infrastructure the current approach of annual mechanized inspections carried out by the Australian Rail Track Corporation ARTC and traditional manual inspections will not be sufficient to address the increasing risks associated with climate change The idea is to explore alternative inspection methods like L iDAR scanning thermal imaging vision systems and audio signal analysis These technologies could significantly improve the efficiency reliability and costeffectiveness of rail infrastructure monitoring better protecting against climateinduced vulnerabilities By incorporating these advanced technologies the CRN can move towards a more proactive maintenance regime capable of detecting potential issues before they lead to failure s The exploration and eventual implementation of these innovative techniques could transform the CRNs maintenance approach making it more responsive to the realities of a changing climate and the essential need for resilient infrastructure systems Problem Statement The Count r y Regional Network in NSW is increasingly vulnerable to climaterelated disruptions which pose risks to operational safety and infrastructure integrity Existing maintenance practices sometimes fall short in addressing these vulnerabilities leading to increased downtime and repair costs Masten 2015 F or example traditional inspection methods may not detect rapid deterioration caused by extreme weather events until significant damage has occurred The challenge lies in identifying and implementing effective maintenance alternatives that integrate advanced technologies such as predictive maintenance and condition monitoring to mitigate risks associated with climate change Baker 2010 Misalignments Misalignments in railway tracks are not inherently caused by hot weather but can occur if there are preexisting issues with the track Properly constructed and maintained tracks should withstand normal temperature ranges without misaligning Key causes include poor track condition poorly maintained or operated trains and insufficient track adjustments Common specific issues include poor rail adjustments the most frequent cause closed rail joints at temperatures below 35ºC loss of adjustment control misaligned rails or welds excessive rail thrust from inadequate fastenings and problematic track geometry like dips or twists Additionally thermal expansion during high temperatures can exacerbate these issues increasing the likelihood of misalignments or track buckling Other contributing factors are weak track structures ballast deficiencies inadequate anchor patterns sleeper movements and disturbances from nearby activities or structural elements like steel sleepers and timber sleeper panels Major disturbances like derailments or improper maintenance actions like partial resleepering can also lead to misalignments particularly under dynamic loads from passing trains or during temperature fluctuations Preventing Misalignment Preventing misalignments in railway tracks involves several critical measures that focus on maintaining track stability throughout the year not just during summer Effective prevention includes UGLRL 2023 Rail Adjustment and Stability Rails should be adjusted to be stressfree at a temperature of 35ºC and securely held in place using anchors fastenings and wellmaintained sleepers on a firm clean and compacted ballast profile Its crucial that rail adjustment is controlled all year round using methods like creep pegs to monitor changes Uncontrolled rail welding must be avoided as it can lead to loss of adjustment control which if unreported remains invisible Therefore all welding activities need to be strictly controlled and documented Welded Track Stability Examination and Analysis This should be conducted before summer to identify and rectify potential misalignment vulnerabilities Defensive Measures Implementing defensive strategies at known vulnerable locations prior to and during the summer to prevent misalignments Summer Work Practices Limiting disturbances to the track especially the bond between the sleeper and the ballast is crucial during hot weather Speed Reductions and Patrols Implementing speed reductions and conducting heat patrols on hot days to monitor and respond to potential track misalignments Incorporating Automated Systems The use of automated systems such as track geometry systems vision systems and audio detection systems is vital in enhancing the precision of inspections and the early detection of misalignments These technologies allow for continuous and detailed monitoring of the tracks condition enabling timely interventions and reducing the likelihood of misalignmentrelated incidents Advanced analytics and machine learning algorithms can further improve the predictive capabilities of these systems identifying patterns that may not be evident through manual inspections Project Aims The main goal of this project is to improve the efficiency accuracy and reliability of rail inspection methods in the Country Regional Network CRN in New South Wales NSW This will be achieved by exploring and incorporating advanced technologies such as Lidar thermal imaging rail temperature prediction models vision systems and audio signal analysis Project Objectives Review and evaluate the current rail inspection techniques used by the CRN to understand their limitations particularly considering the impact of rising temperatures on rail lateral stability Explore the feasibility accuracy and costeffectiveness of alternative rail inspection technologies including Lidar thermal imaging vision systems and audio signal analysis To develop a theoretical model that integrates the most promising technologies into the existing rail inspection framework aiming for a more proactive and predictive maintenance strategy To assess the environmental economic and safety impacts of implementing advanced rail inspection technologies within the CRN Research Questions 1 How can advanced technologies such as Lidar thermal imaging vision systems and audio signal analysis be integrated into the existing rail inspection methodologies of the Country Regional Network to improve the accuracy efficiency and reliability of rail infrastructure assessments 2 What is the potential costbenefit implications of integrating advanced rail inspection technologies into the CRNs maintenance strategy How might the adoption of these technologies affect the environmental footprint of rail infrastructure maintenance and enhance operational safety potentially saving lives and reducing the lifecycle costs of rail assets 3 How does climate change influence maintenance strategies and operational safety in the CRN 4 What innovative maintenance alternatives are currently being employed in the railway sector and how effective are they Outline of Dissertation The dissertation will be structured as follows Chapter 1 Introduction Chapter 2 Literature Review Chapter 3 Methodology Chapter 4 Data Analysis Chapter 5 Discussion Chapter 6 Conclusion and Recommendations Critical Literature Review Introduction NSW Country Regional Network CRN track geometry and stability management UGLRL have an obligation of managing the CRN track in accordance with the suite of standards and meet the contractual reporting requirements as per Operations Maintenance Deed NSW experienced relatively mild summer periods from 201920 to 202223 and during that period the CRN track was not exposed to an increased temperature Whereas the summer period 202324 has been forecasted as extremely hot dry which is likely to test the extremes of the track lateral stability UGLRL 2023 The focus of this literature review is on innovative practices in improving operational efficiency in the CRN in NSW Relevant Theories Formulas and Prior Knowledge Key theories related to maintenance strategies include Reliability Centered Maintenance RCM and Total Productive Maintenance TPM which emphasize the importance of proactive maintenance in enhancing system reliability Moubray 1997 The concept of resilience is also pivotal referring to the ability of systems to adapt to and recover from disruptions Hollnagel 2014 Previous studies have shown that integrating condition monitoring and predictive maintenance can significantly reduce failure rates and maintenance costs Mariusz Kostrzewski 2021 Prior Knowledge Most of the NSW CRN network consists of continuously Welded Rail CWR which provide a range of benefits to operate maintain the network however CWR track has a great risk of track buckling ie misalignment due to the constraints of rail expansion and compression due to temperature changes This is highly concerned and increased risk of track buckling in summer months particularly for disturbed tracks due to insufficient track strength and when the rail adjustment history is unknown for any section of track Track stability is mainly achieved by Sound sleepers which are firmly fastened to the rails and bedded in the standard graded ballast Standard ballast profile which is clean free draining and compacted firmly Effective track structure with resilient fastenings Below are the parameters contributing to the track stability hence provide resistance against track misalignment buckling Sleepers and ballast 60 Fastenings 30 Rails 10 Rail Critical Temperature All CWR track will buckle at a certain rail temperature dependent on the lateral stability resistance to buckling and the internal longitudinal rail stress at temperature The WTSA process is used to identify how much the track lateral stability has decreased and hence how much lower is the expected buckling temperature from the design Critical Buckling Temperature In a typical track structure a loss of track stability of 10 will generally equate to a decrease in the expected buckling air temperature by 4 degrees When considering continued operation care should be taken to consider the expected buckling temperature for any track configuration and the current stability loss to determine risk to train running when temperatures exceed 43⁰C CRN CM 211 Track Geometry Stability Formulas Modelling of Thermal Environment around Rail In this method to calculate the rail temperature The rail is exposed to complex conduction convection and radiation processes that depend on the weather factors such as the air temperature wind speed and cloud and solar factors such as solar irradiance see Figure 2 RTPMs based on the thermal analysis typically predict the rail temperature by balancing the heat transfers Pangjo Chun1 2021 The energy balance equation is given as where 𝐸 sun is the heat flux of the global solar irradiance and 𝐸 conv and 𝐸 rad are the heat fluxes of convection and radiation respectively Figure 2 Thermal Environmental Around the Rail Speed Restriction associated with temperature CRN Hot Weather Speed Restrictions are based on forecast air temperature 38 Degrees Speed Restriction is a temporary reduction in the speed of trains for one day only when the AIR temperature is high or is forecast to be high When the AIR temperature on any day reaches or exceeds 38C OR is predicted to reach or exceed 38C a WOLO Speed Restriction SHALL be applied unless special circumstances apply Note Prior to 15 November the WOLO Speed Restriction SHALL be applied when the AIR temperature reaches or exceeds 35C OR is forecast to reach or exceed 35C This additional restriction is applied because of the general existence of priority Welded Track Stability locations in spring should WTSA be completed the application of 38C may be considered for use by the Civil Maintenance Engineer with appropriate protection applied to identified WTSA defects US Federal Railroad Administration usesused an offset factor of 30F 16C added to rail temp to manage heat speed restrictions CRN standards are built around similar assumptions Figure 2 but written and applied slightly differently Matthew Dick 2016 In reality the offset is not a constant so further work was done by DoTFRA to build a model around rail temperature based on ambient temperature Figure 3 Rail Temperature vs Air Temperature Approach FRA FRA Model Outputs FRA found lessons in what affects rail temperature in local conditions by measuring comparing to model and current practice Figure 4 Figure 4 Role of Local Conditions Heat Transfer Equation Rail Temperature Figure 5 Increase over Ambient Temperature factors include Rail Area exposed to sunlight Sun strength Air temperature Air convection Additional factors Train braking and acceleration sections Figure 5 Rail Temperature Model Temperature Monitoring Devices Methodology Identify locations of mixed heat transfer configuration shaded open North Facing East Facing high maintenance effort curves Apply devices against shaded side of rail web over sleeper Alarm devices for 38 Degrees C alerts provided to Network Control RM AE Primary control remains Track Maintenance and Weather Forecast Log rail temperature data vs forecast air temperature vs recorded air temperature Log weather data over collection period Aim Quantify correlation between air temperature and rail temperature for CRN and identify how it relates to high effort locations Industry Practice Codes and Standards CRN engineering standards provide an assurance framework for managing safety assets and the environment UGLRL 2022 Current industry standards such as those set by the Australian Transport Safety Bureau ATSB and the Rail Industry Safety and Standards Board RISSB provide guidelines for implementing maintenance practices that enhance safety and resilience RISSB 2020 Codes of practice recommend regular risk assessments and adaptive maintenance planning to address climaterelated challenges Transport for NSW 2021 Case Studies Case studies from the Australian railway sector highlight the effectiveness of advanced maintenance technologies For instance the use of drone inspections and automated monitoring systems in the maintenance of rail assets has demonstrated significant improvements in detecting issues before they escalate into major failures Hollis 2020 Additionally the implementation of predictive maintenance in the Sydney Trains network has led to enhanced reliability and reduced operational disruptions Sydney Trains 2021 Aurizon Case Study The first case analysed is in one of Australias largest Rail Operators Aurizon Aurizon is responsible for transporting over 250 Million tonnes of commodities each year During hot weather events the rail operator holds the increased risk of the track buckling and derailment posing a danger to operator and community safety and the environment To prevent this from occurring the rail operator imposes speed restrictions when the air temperature reaches 40C To determine when speed restrictions need to be applied weather data was previously sourced from the Bureau of Meteorology The data was obtained from the closest weather station to each section of rail network which could be over 100km away With this challenge the rail operator sought to segment their network into zones and undertake their own localised track and ambient temperature monitoring Kallipr 2022 The Captis Solar solution was selected for monitoring ambient and rail track temperatures along the rail network using NBIoT technology enabling reliable data transmission from remote locations Its IP68rated enclosure ensures durability against extreme conditions The device features edge processing alarm handling and a rechargeable battery allowing adaptive data transmission frequencies based on realtime conditions Data logging was set to every 15 minutes with the frequency increasing to every minute during extreme temperature events This facilitated timely decisionmaking on applying localized speed restrictions significantly reducing delays and saving the rail operator over 25 million in five months Encouraged by these results the rail operator plans to expand the use of Captis Recharge across the entire network Kallipr 2022 The Captis Solar system exemplifies a successful application of IoT technology in critical infrastructure management The ability to monitor and react to realtime environmental changes with precision highlights the potential of IoT devices in enhancing operational efficiency and safety The strategic deployment of such technology not only improves resource allocation by avoiding blanket restrictions but also significantly cuts operational costs This case study serves as a robust model for similar applications in other sectors where environmental conditions impact operational efficiency Moreover the scalability of the solution as indicated by the planned networkwide deployment demonstrates its efficacy and adaptability potentially setting a standard for industry practices in infrastructure management Research Gap and Potential impact Significances Despite significant advancements in maintenance technologies globally there exists a notable gap in comprehensive frameworks that address the unique challenges of integrating these technologies into the NSW Country Regional Network CRN The current literature and industry practices lack tailored solutions that consider the specific operational environmental and climatic conditions of the CRN This gap is particularly critical given the predicted extreme temperatures for the summer of 202324 which heighten the risks of track buckling and derailments This research seeks to bridge this gap by developing a detailed framework that guides the effective integration of advanced maintenance technologies into the CRNs existing infrastructure By doing so it aims to provide railway operators with actionable insights that enhance maintenance practices improve operational efficiency and bolster resilience against the adverse impacts of climate change Ultimately this contribution is expected to lead to safer and more efficient rail operations setting a precedent for similar networks facing comparable challenges Identifying specific Research Gaps A significant research gap identified is the lack of comprehensive frameworks for using audiofrequency analysis to detect rail defects within the NSW CRN There is currently no research in NSW focused on this approach nor are there any offtheshelf typeapproved products available for immediate implementation Additionally integrating such audiofrequency systems with existing maintenance methods poses a challenge due to the absence of established guidelines or protocols for their interaction Hypothesis The research will hypothesize that the integration of advanced maintenance technologies significantly improves operational efficiency and resilience in the face of climaterelated challenges within the CRN One of the proposed methods is to use the XPT passenger trains to collect data where an ECMX78MP Microphone F igure 6 is used to collect acoustic signal The MelFrequency cepstrum coefficients MFCC features from the acoustic signals are later used with different classifiers for the automatic detection of track faults The Scope of this work is confined to detecting railway track faults using acoustic analysis Li 2016 Figure 6 Sony ECMX7BMP Electret Condenser Lavalier Microphone for UWP Transmitters The second method would be using another Railway Track Inspection Systems from another vendor Figure 7 t hat can detect Superelevationtrack gauge and possible a vision system to detect rail cracks The intent would be to cross check this data against the Acoustic signals and the exist data of the AK Car service track geometry data Figure 7 Track Geometry Measurement Systems TGMS DMA Torino The third and final method will be Temperature Monitoring Devices Figure 8 in the CRN by Identify locations of mixed heat transfer configuration shaded open North Facing East Facing high maintenance effort curves Apply devices against shaded side of rail web over sleeper Alarm devices for 38 Degrees C alerts provided to Network Control RM AE Primary control remains Track Maintenance and Weather Forecast Log rail temperature data vs forecast air temperature vs recorded air temperature Log weather data over collection period Aim Quantify correlation between air temperature and rail temperature for CRN and identify how it relates to high effort locations Figure 8 CRM3000 to Monitor Temperature 27 Summary of Literature Review The literature review explores the management of track geometry and stability in the NSW Country Regional Network CRN focusing on innovative practices to improve operational efficiency The review highlights the importance of continuous welded rail CWR in the network and the associated risks of track buckling especially under extreme temperature conditions predicted for the summer of 202324 Key maintenance strategies discussed include Reliability Centered Maintenance RCM and Total Productive Maintenance TPM which emphasize proactive maintenance to enhance system reliability The concept of resilience crucial for systems to adapt and recover from disruptions is also covered The literature points to the significant benefits of integrating condition monitoring and predictive maintenance to reduce failure rates and maintenance costs The review delves into the technical aspects of rail stability emphasizing the critical role of sleepers ballast and fastenings in maintaining track alignment and preventing buckling It also discusses the formulas and models used to calculate rail temperature considering factors like solar irradiance air temperature and wind speed Industry practices and standards from organizations like the Australian Transport Safety Bureau ATSB and the Rail Industry Safety and Standards Board RISSB are examined along with case studies demonstrating the effectiveness of advanced maintenance technologies such as drone inspections and predictive maintenance A notable case study involves Aurizon an Australian rail operator which implemented the Captis Solar solution for monitoring ambient and rail track temperatures to make realtime decisions on speed restrictions This IoTbased solution significantly reduced operational delays and costs highlighting the potential of technology to enhance operational efficiency and safety The literature review identifies a research gap in comprehensive frameworks for integrating advanced maintenance technologies into the CRN proposing further research to enhance maintenance practices and resilience against climate change impacts The review hypothesizes that the integration of these technologies can significantly improve operational efficiency and resilience within the CRN Methodology Introduction This chapter outlines the research methodology employed to investigate the impacts of climate change on maintenance practices in the CRN focusing on the integration of innovative technologies and stakeholder perspectives This research aims to contribute to the Maintenance Asset Management fields by studying and analysing different measurement system in the rail environment By grouping knowledge from various engineering disciplines the study will propose a comprehensive system that leverages Lidar thermal imaging vision systems and audio signal analysis for enhanced rail infrastructure monitoring This research could turn into a prototype measurement system Explore Research Methodological Approaches A mixedmethods approach will be employed combining quantitative data analysis with qualitative case studies This methodology will incorporate experimental methods for testing new technologies and case studies for understanding current practices Creswell 2018 Integration of Advanced Technologies Explore the integration o track geometry system vision systems and audio signal analysis into the CRNs existing rail inspection methodologies through a comparative analysis Research on Audio Signal Analysis Device Development study the use of audio signals aiming to detect rail stress stability issues and other defects Data Analysis and Validation Compare simulation results with data from existing literature or case studies using track geometry data within the Country Regional Network CRN to confirm the effectiveness of the proposed methodologies Identify Select and Justify Research Methodological Approach A mixedmethods approach is justified as it allows for a comprehensive analysis of both statistical data regarding maintenance performance and qualitative insights from stakeholders involved in the CRN This integration enhances the validity and reliability of the research findings Flick 2018 Outline the Details of the Methodology Data will be collected from historical data and from track geometry data available analysis of maintenance records and case studies of technological implementations in the CRN Surveys will gather information on current practices challenges and the perceived effectiveness of innovative technologies AudioFrequency Analysis Method Equipment Setup Temporary Installation of the ECMX78MP microphone on XPT passenger train to test the system mounting mechanism to be confirmed and requirements to calibration procedure Data Collection Routes and Scheduling Most likely the data collected will be from the Main west line Orange to Dubbo in NSW Data Logging data will be recorded with a mobile phone Signal Processing Preprocessing Noise reduction techniques to filter out irrelevant sounds Will Use MelFrequency Cepstral Coefficients MFCC to extract features from acoustic signals Training and Validation Use a dataset of known track faults to train models and validate their accuracy Analysis Fault Detection will Identify patterns correlating with specific track defects Performance Metrics Evaluate the system using accuracy precision recall and F1score Track Geometry Measurement Systems Supplier research conduct research for vendor selection based on systems capabilities cost effectiveness and compare to the current service from AK CAR Data collection collect data from AK car and compared it to vendor samples Analyse and compare parameters measured such as Superelevation track gauge alignment and rail wear Data analysis comparison with acoustic data Crossvalidate possible findings from both systems and their limitations Utilise GIS tools to map defects spatially Temperature Monitoring Devices Location selection Criteria choosing for monitoring sides based on factor like shading and know hotspots Data Logging and Alerts Threshold settings based on CRN requirements set alarms for critical temperatures eg 38 degrees Celsius Data Transmission methods for realtome data transmission to Network control andor Maintenance teams Methods Processes Software and Tools Excel and PowerBi will be utili s ed for data analysis while the data from the CRN asset register GIS tools will help in visualizing maintenance needs and vulnerabilities across the CRN ESRI 2023 Additionally tools for condition monitoring and predictive analytics will be employed to assess maintenance strategies Possible Software Tools Data Analysis MATLAB Python with libraries like NumPy Pandas SciPy R Signal Processing MATLAB Signal Processing Toolbox Pythons LibROSA Machine Learning Pythons Scikitlearn TensorFlow Keras GIS Software CRN ArcGIS Data Visualization Power BI for creating interactive dashboards Processes Data Preprocessing Cleaning and normalizing data before analysis Model Training Steps for training and testing machine learning models Validation Techniques Crossvalidation confusion matrices ROC curves Expected Outcomes Enhanced Inspection Methodologies Development of a costeffective efficient and reliable rail inspection methodology that integrates advanced Mechatronics technologies CostBenefit Analysis Offer detailed insights into the economic advantages including potential savings in maintenance and reduced downtime and environmental gains through optimized resource usage Risk Assessment Identify possible risks linked to the use of new technologies including technological operational and environmental risks and strategies to mitigate them By achieving these outcomes the research aims to provide a strategic framework for the CRN to enhance maintenance practices improve safety and increase resilience against climate change impacts Limitations of the Study An anticipated limitation of the study is the potential challenge in installing the ECMX78MP microphone on the XPT passenger trains due to operational and logistical constraints As an alternative the microphone could be temporarily installed on the AK car during its Track Geometry inspections providing a feasible platform for data collection without disrupting regular passenger services Additionally the research may encounter data constraints such as limited access to certain datasets or variability in data quality which could impact the depth of the analysis Technological challenges might arise from equipment reliability issues or integration difficulties when interfacing new monitoring systems with existing infrastructure Furthermore the findings of this study may be specific to the CRN and not directly applicable to other railway networks which could limit the generalizability of the results Timeline and Project Management Project Phases Phase 1 Literature review and initial planning Phase 2 Equipment acquisition and setup Phase 3 Data collection and preliminary analysis Phase 4 Model development and testing Phase 5 Final analysis and reporting Results and Analysis Remote Temperature Sensor The initial remote rail temperature sensor trial will be utilising approximately 10 units of CRM 3000 devices as pictured below Placement and initial selection will be carried out by the Senior Track and Civil Engineer The CRM 3000 is battery powered and magnetically attached to the rail The device measures and transmits the following information over the 4G Optus network to a central data repository Rail Temperature at attachment point Device Internal Temperature Device Accelerometer data Device Network Signal Strength Placement The CRM 3000 is to be placed on the inside web of the rail clear of any ballast fasteners or fixed assets such as level crossings bridges and signalling equipment The rail where the device is to be placed should be clear of dirt or debris and should be lightly scrubbed to ensure good contact between the device and the rail The CRN 3000 is to be placed above a sleeper rather than between sleepers to avoid interference from tamping machine tines and clamps The sleeper and rail where the CRM 3000 have been installed are to be marked with white spray paint and paint pens respectively indicating the presence of a trackside measuring device As the CRM 3000 relies on the 4G Optus network to transmit data ensure that the chosen installation location is covered by the 4G Optus network and validate data transmission following installation Maintenance Requirements The CRM 3000 is expected to last for up to 5 years in track without battery or maintenance intervention The device is fully sealed and potted and is not expected to deteriorate from heat frost dust or insects If for any reason track maintenance or operations works require the CRM 3000 to be removed or relocated the device may be removed from the web of the rail by hand The device can then either be repositioned to the inside web of the rail at a nearby location on the same rail leg above sleeper if appropriate and moving within 20m or returned to the nearest track maintenance depot superintendent In either case the Senior Track Engineer is to be advised of the change Device Hazards As the CRM 3000 device is magnetically attached at the rail surface on the inside of the rail web the device does not interfere with the structural stability of the rail or track system The following additional hazards and controls have been identified in the installation operation and deactivation of the device Activity Hazard Control Installing and removing CRM3000 on rail Pinch point between CRM3000 and Rail due to magnetic attraction leading to injury Installation to be done by hand with firm grip on device Device to be handled with care and kept clear of electronics Device marked with warning label regarding magnetic risks Interference with Plant CRM3000 crushed with tamper clamps excavator or other rail plant leading to damage to device Devices to be placed above sleeper on inside web of rail to minimize interference with rail plan Location to be marked with spray paintpen Rail plant teams to visually inspect work area prior to works which impact rail and moveremove devices accordingly These hazards to not include any rail safety risks during installation as these are to be assessed as part of a site risk assessment This Technical Note also excludes any operational risks driven by the data captured through the device as this is a preliminary trial and does not supersede any existing standards or controls on management of track stability or rail temperature 45 Device Operation 451 Calibration and Testing Requirements In order to assess the suitability of the CRM3000 for the CRN the temperature sensor is to be spotchecked against a traditional rail thermometer This will occur on installation with the installer to note the time and rail temperature adjacent to the device following installation using a calibrated rail thermometer and advise the Senior Track and Civil Engineer Rail temperature measurement are to also be undertaken as spot checks on track walks and hirail patrols This will be managed by the Senior Track and Civil Engineer through collaborations with the Track Maintenance Depot Superintendents Where the difference between the measured temperature and the temperature reported by the CRM 3000 differs the measured temperature is to be taken as correct Where this difference regularly exceeds 15 ⁰ C the CRM 3000 is to be removed issues noted and causes such as excessive rust on the rail web separation any issues related to the sensor itself assessed Hot Weather Alarms CRM 3000 devices include the implementation of upper bound and lower bound email alarms based on the recorded temperature of the rail The device is unable to record ambient air temperature CRN CM 211 Track Geometry and Stability stipulates track stability requirements based on Air Temperature As a result the alarms function of the CRM 3000 will be advisory only and set at two thresholds as follows Table 1 Alert Parameters Alert Validity Period Sensor Rail Temperature Threshold Determining Method Response Amber OOS Out of Season Prior to 15 November 48 ⁰ C Nominal alert air temperature of 33 ⁰ C 1 15 ⁰ C 2 temperature increase for exposed rail Advisory alert to the Civil Maintenance Engineer CME Senior Track and Civil Engineer Maintenance Superintendent by email Maintenance Superintendent andor Senior Track Civil Engineer to review current and forecasted air temperature from appropriate BoM Offices or CRN remote monitoring stations and confirm and document that either Appropriate controls are or will be in place as per CRN CM 211 Controls are not required based on air temperature forecast and understanding of local conditions Where it is suspected that forecasted air temperatures are not representative of local conditions at the recording site the CME is to apply controls as required based on local knowledge and on consultation with the Maintenance Superintendent Red OOS Out of Season Prior to 15 November 50 ⁰ C PreSummer WOLO air temperature of 35 ⁰ C 1 15 ⁰ C 2 temperature increase for exposed rail Amber Alert From 15 November 51 ⁰ C Nominal alert air temperature 36 ⁰C 1 15 ⁰ C 2 temperature increase for exposed rail Red Alert From 15 November 53 ⁰ C 38 ⁰ C WOLO air temperature 15 ⁰ C 2 temperature increase for exposed rail Notes 1 A nominal alert temperature was selected for Amber Alerts 2 C less than the standard WOLO temperature to provide sufficient buffer from Red Alert temperatures and ensure that there is sufficient time for the sensor to transmit data and for Engineering and Maintenance personnel to review the alert 2 A rail to air temperature increase of 15 C is typically observed in rail that is exposed to full sunlight This observation is mirrored in work undertaken by the FRA ENSCO Rail WRI 2016 however should be continuously assessed as part of this trial rollout The installation of the CRM 3000 devices will NOT override existing requirements for track stability management rather this will be used as additional secondary tool The measured rail temperature does not carry a linear relationship to air temperature and is typically correlated to UV exposure surface area exposed to sunlight and operating conditions such as train braking locations The table below outlines an expected range to be reassessed on collection of 1 year of data from the CRM 3000 devices Table 2 Hypothesis Air rail Temperature Correlation Air Temperature Expected Rail Temperature Expected Variance in Rail Temperature compared to Air Temperature 0 ⁰ C 0 ⁰ C 5 ⁰ C 5 ⁰ C 5 ⁰ C 5 ⁰ C 10 ⁰ C 15 ⁰ C 5 ⁰ C 15 ⁰ C 25 ⁰ C 5 ⁰ C 20 ⁰ C 30 ⁰ C 7 ⁰ C 25 ⁰ C 40 ⁰ C 7 ⁰ C 30 ⁰ C 45 ⁰ C 10 ⁰ C 35 ⁰ C 50 ⁰ C 10 ⁰ C 38 ⁰ C 53 ⁰ C 10 ⁰ C 40⁰ C 55 ⁰ C 10 ⁰ C 50⁰ C 65⁰ C 10 ⁰ C 47 Performance Assessment Criteria The performance of the CRM 3000 devices will be measured as follows Table 3 Performance Assessment Metrics Metric Measurement Expected Result Accuracy Variance when measured against rail thermometer 15 ⁰ C Accuracy against Forecast Variance of daily maximum recorded rail temperature to forecasted air temperature 15 ⁰ C Daily maximum air temperature to be collected from BOM daily temperature observations for region 10 ⁰ C Variance in rail temperature recorded by the sensor to the air temperature 15 C Forecasting methodology Number of CRM 3000 alarms raised as per Section 32 of this document coinciding with raising of WOLO alarms through current methodology 100 Maintenance Number of units affected by maintenance activities movement from position andor return to depot 0 During this trial phase the CRM 3000 sensors will not override existing procedures for management of rail stress and associated risks 48 Trial Period The trial period is 1 year for the rail temperature sensor After trial period Engineering team will assess the performance of the CRM 3000 49 List of Installation Locations The following table outlines the proposed number of CRM 3000 devices to be initially deployed across regions This is expected to change and exact locations to be continuously amended to suit trial methodology and impose variability to test the CRM 3000 Table 4 Unit Assignment Locality Number of CRM 3000 units assigned Wallerawang to Orange 3 Orange to Dubbo 2 Wallerawang to Rylstone 2 Werris Creek to Tamworth 1 Queanbeyan to Canberra 2 Placement of devices is subject to onsite factors such as access and 4G network availability A log of installation locations is to be kept by the Senior Track and Civil Engineer 5 Simulation and Comparison T o support the hypothesis and provide a computational counterpart to the temperature data collected in the field a steadystate thermal simulation was developed using ANSYS Workbench The purpose of this simulation was to estimate the thermal distribution across a section of continuously welded rail exposed to summer conditions representative of NSWs climate Simulation Setup Objective Simulate rail temperature on a 35C summer day to compare against CRM3000 rail thermometer measurements and validate heat transfer assumptions Geometry The model used a realistic rail profile with a small expansion gap covering approximately 07 m in total length Analysis Type SteadyState Thermal Duration 1 s static thermal equilibrium Units SI Boundary Conditions A Convection Applied across all side surfaces not exposed to solar flux Ambient temperature 35 C Film coefficient 10 Wm²C This assumption corresponds to natural convection in still air consistent with literature values for lowwind scenarios ASHRAE Handbook 2021 B Heat Flux Applied to the top rail head surface Value 1200 Wm² Justified by peak solar irradiance measurements in Australian summer conditions average global horizontal irradiance ranges between 10001200 Wm² per BOM and PVGIS databases C Radiation Surfacetoambient radiation considered Ambient temperature 35 C Emissivity 085 typical for oxidized steel surfaces or painted railheads DoE Surface Emissivity Tables Pangjo Chun et al 2021 Mesh and Solution Quality The mesh used tetrahedral elements with refinement along the railhead to better capture gradients due to solar exposure The convergence was verified based on residuals and negligible variation in temperature field with further mesh refinement Results Summary Minimum Temperature 494C lower rail base Maximum Temperature 533C top rail head A clear thermal gradient was observed from the rail base convectiondominated to the rail head solar exposuredominated These results align well with observed field conditions and expectations For example CRN thermal alerts are triggered at 53C see CRM3000 device calibration guidelines and railtoair deltas of 15C are commonly recorded by field thermometers during full sun exposure Dick ENSCO Rail 2016 Validation and Comparison The simulated peak temperature 533C at an ambient of 35C matches closely with historical CRN thermometer readings and WOLO thresholds Studies by the US Federal Railroad Administration FRA and CRN documents CRN CM 211 confirm typical temperature rise of 1215C above ambient under direct sunlight Recommendations for Future Work Visual Verification Supplement with thermal imaging photography during peak sun to crossverify simulated thermal zones TimeDependent Studies Extend to transient simulations using solar cycle data eg morning to afternoon profiles Surface Finish Variability Perform sensitivity analysis on emissivity 0309 to replicate conditions like polished rail or oxidizedweathered surfaces Wind Speed Effects Modify the convection coefficient 1025 Wm²C based on local wind conditions BOM station data Rail Coating and Material Tests Incorporate different materials or coatings eg anticorrosive layers to observe impact on heat retention Suggested Photos and Visual Documentation To enhance the quality and credibility of the study Include thermal imaging photos of the rail during summer midday eg FLIR or infrared camera snapshot of rail profile Add sidebyside comparison between ANSYS simulation and CRM3000 temperature plots Display device placement images showing real installation context and orientation Provide graph overlays of simulation temperature vs rail thermometer readings CRM3000 data logs These steps will reinforce the validity of the simulation and improve overall understanding of the heat behavior in rail infrastructure during peak summer periods Benefits Risks and Consequences Conclusion References State of Environmental SOE 2021 Climate Change Impacts on Rail Infrastructure Available at httpswwwsoeepanswgovausitesdefaultfiles20220221p3448nswstateoftheenvironment20210pdf Accessed 13032024 This report provides foundational insights into how rising temperatures compromise rail infrastructure integrity reinforcing the need for advanced inspection methods Its critical for understanding environmental impacts on rail safety and reliability informing the development of temperatureresilient inspection technologies This report will be fundamental to have a better understand of the impact on the Country Regional Network in NSW Pear ce C 2020 Railway Engineering in a Changing Climate Available at httpswwwrailexpresscomautheeffectextremeweatherrailtrackinfrastructure Accessed 25 02 2024 Pearces research highlights how rising temperatures increase vulnerabilities in rail infrastructure leading to issues like track buckling and material degradation that compromise safety and efficiency Given the climatic diversity of the Country Regional Network CRN in New South Wales the findings support a shift towards more frequent and automated inspection regimes Using advanced sensors and realtime data automated technologies enhance monitoring early detection and preventive maintenance improving the CRNs operational integrity and costefficiency Lee A Patel H Thompson M 2021 Exploring the effectiveness of thermal imaging in rail track inspections in Proceedings of the International Conference on Railway Engineering Chicago 2022 October pp 234245 The study on the effectiveness of thermal imaging for rail track inspections is particularly relevant for the Country Regional Network CRN of NSW where temperatures can soar up to 45 degrees Celsius Thermal imaging can detect heatinduced deformities and stress in tracks before they become critical providing crucial data for maintenance This technology enables the CRN to manage track integrity proactively enhancing safety and reducing the risk of heatrelated rail failures in extreme temperatures MATLAB Simulink 2020 ModelBased Design for Rail Systems Available at httpsaumathworkscomsolutionsrailwaysystemshtml Accessed 23 02 2024 The MATLAB Simulink resource on ModelBased Design for Rail Systems provides invaluable insights into how simulation software can optimize rail system designs By applying these tools engineers can virtually design test and refine rail inspection systems under various environmental and operational conditions ensuring robustness and efficiency before physical deployment This approach is critical for adapting rail systems to diverse challenges and enhancing their reliability and safety Kostrzewski M Melni k R Paś 2021 Condition Monitoring of Rail Transport Systems A Bibliometric Performance Analysis and Systematic Literature Review This paper elucidates the evolution of condition monitoring in rail transport spotlighting the shift towards automated and sensordriven maintenance As rail systems integrate more sophisticated technologies like thermal imaging LiDAR vision systems and acoustic monitoring theres an enhanced capacity to identify and validate defects beyond traditional track geometry assessments This comprehensive approach allows for more precise diagnostics and proactive maintenance strategies crucial for the Country Regional Network Jaskólski K Specht C 2022 Preparatory Railway Track Geometry Estimation Based on GNSS and IMU Systems Remote Sensing 1421 p5472 This study highlights the integration of GNSS receivers and IMU systems into rail transport emphasizing their role in accurately assessing track geometry Such advancements mark significant progress in the evolution of condition monitoring underscoring the necessity of diversifying rail inspection methods By incorporating these technologies rail systems benefit from enhanced precision in monitoring infrastructure conditions leading to improved maintenance strategies and increased operational safety Zhang L Wang K Zhou Q 2022 Correlation Analysis between Rail Track Geometry and CarBody Vibration Based on Fractal Theory Fractal and Fractional 612 p727 This article demonstrates how fractal analysis can elucidate the relationship between track irregularities and carbody vibrations offering important insights for highspeed rail maintenance Understanding this correlation better can significantly enhance maintenance strategies focusing on ride quality Such precision in identifying where and how tracks affect ride stability could lead to more targeted effective maintenance interventions improving both safety and passenger comfort in highspeed rail systems Li X Feng D Li Z De Roeck G 2022 Prediction Models for Railway Track Geometry Degradation Using Machine Learning Methods A Review Sensors 2219 p7275 The paper reviews machine learning methods for predicting railway track degradation highlighting the effectiveness of ANN SVM and GM in understanding and forecasting track condition deterioration Integrating advanced machine learning methods into the CRNs track inspection systems can transform how maintenance is planned and executed particularly in adapting to and mitigating the effects of rising temperatures on rail infrastructure Bowyer C Rietveld P Ortega E 2020 Climate services for the railway sector A synthesis of adaptation information needs in Europe Frontiers in Environmental Science This study emphasi s es the critical need for climate adaptation in the rail sector highlighting significant knowledge gaps and the mismatch between adaptation needs and available information Understanding these gaps can guide the development of new technologies or methodologies tailored to the specific climatic conditions of New South Wales ensuring that the track inspection systems are both effective and efficient under increased thermal stress Valix M 2021 How climate change impacts infrastructure experts explain The University of Sydney Professor Valix discusses how climate change impacts watercarrying infrastructure through variations in precipitation sea levels and temperatures underlining the broader implications for infrastructure resilience Understanding the broader implications of climate change including flooding and extreme temperatures can help the CRN to develop inspection systems that are resilient to a wider range of environmental challenges This is crucial for maintaining service reliability and safety in the face of climateinduced weather events Ariyaratne W 2021 Bridges to be bolstered to survive climate change The University of Sydney Professor Ariyaratne explores the necessity of strengthening bridge infrastructure to withstand the adverse effects of climate change including extreme temperatures and higher flood levels This article provides interesting insights that could be use when conducting the research on the impacts of clime change in the Country Regional network in NSW Marks B 2021 Will a changing climate cause more landslides The University of Sydney Dr Marks addresses the impact of changing rainfall patterns on the Australian landscape particularly the risk of landslides along the Great Dividing Range due to climate change The research underlines the necessity for adaptive strategies in railway maintenance and operations to cope with the impacts of climate change This could lead to the development of new guidelines and standards for railway infrastructure resilience in regions susceptible to climateinduced landslides Brown R Kumar S 2022 Novel methodologies in track geometry inspection using LiDAR technology Journal of Rail Transport Planning Management 123 pp 145162 Novel methodologies in track geometry inspection using LiDAR technology Journal of Rail Transport Planning Management Potentially the methodologies discussed in Brown and Kumars study into the CRNs track inspection systems could revolutionise how track integrity is maintained in response to the increasing challenges posed by climate change making rail travel safer and more reliable Siddique MA Lee E Ashraf I Dudley S 2021 A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis Sensors 2118 p6221 This study introduces an innovative system for detecting railway track faults through acoustic analysis applying machine learning algorithms to classify data from acoustic signals It showcases the potential of this method in enhancing train operation safety by automating the detection of common track issues Automated and accurate detection of faults through acoustic analysis can help optimise maintenance schedules in the CRN focusing efforts where they are most needed and preventing unnecessary maintenance activities This targeted approach can reduce overall maintenance costs and downtime Smith J Zhao L 2023 Innovations in Railway Track Inspection A Comprehensive Approach 1st ed London Routledge Smith and Zhaos book details cuttingedge railway track inspection technologies providing a comprehensive approach to modern inspection methods For the Country Regional Network in NSW these innovations could bring enhanced safety reduced maintenance costs and improved operational efficiency By adopting advanced technologies the network can proactively manage track integrity particularly beneficial in the face of increasing environmental stresses ARTC 2020 Climate Resilience Program Australian Rail Track Corporation Retrieved from ARTC Dey S et al 2020 Probabilistic Risk Assessment in the Railway Sector Safety Science 128 104723 Creswell J W Plano Clark V L 2018 Designing and Conducting Mixed Methods Research SAGE Publications ESRI 2023 Using GIS for Climate Resilience in Transportation Environmental Systems Research Institute Retrieved from ESRI Fang Y et al 2017 Resilience Engineering Theory and Applications Safety Science 93 128137 Flick U 2018 An Introduction to Qualitative Research SAGE Publications Hollis M et al 2020 Innovative Technologies in Railway Maintenance A Review Journal of Rail Transport Planning Management 12 100150 Klein R J T et al 2020 Adaptation to Climate Change in the Australian Railway Sector Journal of Infrastructure Systems 263 04020021 Li Q et al 2021 Predictive Maintenance Technologies for Rail Infrastructure A Review IEEE Transactions on Intelligent Transportation Systems 223 17551768 Masten S E et al 2015 Reliability Centered Maintenance for Railway Systems Journal of Rail Transport Planning Management 53 90104 Moubray J 1997 Reliability Centered Maintenance Industrial Press RISSB 2020 Rail Industry Safety and Standards Rail Industry Safety and Standards Board Retrieved from RISSB Sydney Trains 2021 Innovative Maintenance Practices and Technologies Retrieved from Sydney Trains Transport for NSW 2021 Climate Adaptation Framework for the Transport Sector Retrieved from Transport for NSW UGLRL 2022 CRN MANCVL713026361707 TRACK GEOMETRY STABILITY The document details standard practices for maintaining track geometry and stability emphasizing the importance of precise measurements and adjustments to prevent misalignments and ensure safe train operations It also covers the management of track geometry under different temperature conditions highlighting strategies for summer and winter maintenance UGLRL 2023 Special Summer Time Instructions 202324 This document outlines the procedures for managing track stability during the hot summer months in NSW indicating the critical role of temperature in track alignment and stability It discusses various components like sleepers ballast and rail anchors which contribute significantly to track stability Beaureau of Metereology 2020 State of Climate 20220 httpwwwbomgovaustateoftheclimate2020 Accessed 03 11 2024 Pangjo Chun1 Yun Mook 2021 A RailTemperaturePrediction Model Based on Machine Learning Warning of TrainSpeed Restrictions Using Weather Forecasting Dick ENSCO Rail 2016 Comparison of Predicted and Actual Rail Temperature 2015 Li Q Zhang Y Wu Q 2016 Railway Wheel Flat Detection Based on Acoustic Signals Applied Acoustics 113 1220 This study explores the use of acoustic signals and MFCCs for detecting defects in railway wheels which is analogous to track fault detection 2