Prescriptive Maintenance of Railway Infrastructure
- 2. Prescriptive Maintenance of Railway Infrastructure: From Data Analytics to Decision Support 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) 05-07 June 2019 Authors Alice Consilvio, Paolo Sanetti, Davide Anguìta, Carlo Crovetto, Carlo Dambra, Luca Oneto, Federico Papa and Nicola Sacco DIME, University of Genoa, Genoa, Italy
- 3. Introduction • Railway Infrastructure Maintenance • Data Collection: The process begins with the collection of field data from railway signaling systems, including sensor readings, equipment status, and performance metrics. • Data Preparation: The collected data is then prepared for analysis, involving cleaning, normalization, and structuring to ensure its suitability for further processing. • Analytics Extraction: Advanced analytics are applied to extract valuable insights and knowledge regarding the current and future status of railway assets.
- 4. Introduction • Decisional Process Overview • Diagnostic Aspect: The prescriptive maintenance framework covers diagnostic aspects, involving the identification of current issues and potential areas of concern within the railway infrastructure. • Predictive Aspect: It also encompasses predictive maintenance, utilizing historical and real-time data to forecast potential failures and maintenance requirements. • Prescriptive Aspect: The framework optimizes maintenance activities by merging historical and real-time data, resulting in proactive and optimized maintenance plans.
- 5. Introduction • Knowledge Utilization • Knowledge Application: The extracted knowledge from the analytics phase is utilized to make informed decisions regarding maintenance strategies and resource allocation. • Optimization Strategies: Prescriptive maintenance enables the optimization of maintenance activities, ensuring cost-effective and efficient utilization of resources. • Decision Support: The knowledge derived from data analytics serves as a foundation for decision support, guiding maintenance actions and resource allocation.
- 6. Introduction • Real-Time Decision Support • Real-Time Data Integration: The prescriptive maintenance process involves the integration of real-time data to enable dynamic decision support and adaptive maintenance strategies. • Proactive Planning: It facilitates proactive planning by leveraging real-time data insights to anticipate and address potential maintenance requirements. • Adaptive Maintenance: The decision support system adapts to real- time changes in asset conditions, enabling agile and responsive maintenance interventions.
- 7. Methods/Tools and Techniques Used in Each Research • Data Acquisition and Integration • Sensor Data Collection: Various sensors installed in railway infrastructure capture critical data related to equipment performance, environmental conditions, and operational parameters. • Data Fusion: The collected sensor data is fused and integrated to create a comprehensive dataset that provides a holistic view of the railway infrastructure's health and operational status. • Historical Data Incorporation: Historical maintenance records and performance data are integrated with real-time sensor data to enrich the analytics process.
- 8. Methods/Tools and Techniques Used in Each Research • Predictive Modeling • Machine Learning Algorithms: Advanced machine learning algorithms are employed to develop predictive models that forecast potential failures and degradation patterns in railway assets. • Anomaly Detection: The predictive models incorporate anomaly detection techniques to identify irregularities and deviations from normal operational behavior. • Failure Prognostics: The models enable the prognostication of potential failures, allowing for proactive maintenance interventions to prevent operational disruptions.
- 9. Methods/Tools and Techniques Used in Each Research • Asset Health Monitoring • Continuous Monitoring: The data-driven analytics framework facilitates continuous monitoring of asset health, enabling the early detection of performance degradation and impending issues. • Performance Trend Analysis: Historical and real-time data are analyzed to identify performance trends and patterns, providing insights into the long-term health of railway assets. • Condition-Based Maintenance: The analytics process supports the implementation of condition-based maintenance strategies, optimizing resource allocation based on asset health assessments.
- 10. Methods/Tools and Techniques Used in Each Research • Optimization Strategies • Resource Allocation: Data-driven analytics guides the optimal allocation of maintenance resources, ensuring that interventions are prioritized based on asset criticality and operational impact. • Cost-Efficiency: The framework emphasizes cost-efficient maintenance practices, leveraging data insights to minimize operational costs while maximizing asset reliability. • Performance Enhancement: By identifying performance improvement opportunities, the analytics process contributes to enhancing the overall operational efficiency of railway infrastructure.
- 11. Results and Discussion • Proactive Maintenance Planning • Prognostic Insights: The decision support system provides prognostic insights, enabling the proactive planning of maintenance activities to address potential asset degradation. • Resource Optimization: It facilitates the optimization of maintenance resources by aligning interventions with predicted asset health trajectories and operational demands. • Risk Mitigation: The system supports risk mitigation by identifying and addressing potential safety and operational risks through proactive maintenance planning.
- 12. Results and Discussion • Adaptive Maintenance Strategies • Real-Time Adaptation: The decision support system dynamically adapts maintenance strategies based on real-time data inputs, ensuring agile responses to changing asset conditions. • Operational Flexibility: It enhances operational flexibility by enabling adjustments to maintenance schedules and activities in response to evolving asset performance indicators. • Performance Feedback Loop: The system incorporates a feedback loop that integrates maintenance outcomes to refine and optimize future maintenance strategies.
- 13. Results and Discussion • Resource Allocation Optimization • Dynamic Resource Allocation: It supports dynamic resource allocation, allowing for the reallocation of maintenance resources based on shifting asset health priorities and operational requirements. • Efficiency Metrics: The decision support system incorporates efficiency metrics to evaluate the effectiveness of resource allocation and maintenance interventions. • Performance Monitoring: It provides real-time monitoring of maintenance performance, enabling continuous assessment and refinement of resource allocation strategies.
- 14. Results and Discussion • Operational Intelligence • Operational Insights: The system generates operational intelligence by synthesizing data analytics and maintenance outcomes, offering actionable insights for operational enhancement. • Performance Benchmarking: It facilitates benchmarking of maintenance performance against predefined targets and industry standards, driving continuous improvement initiatives. • Operational Feedback Loop: The decision support system establishes a feedback loop that integrates operational insights into future maintenance and operational decision-making processes.
- 15. Conclusion • It emphasizes the implementation of a prescriptive maintenance framework for Intelligent Asset Management System (IAMS) in the railway sector, leveraging data-driven analytics and optimization algorithms. • The study focuses on the shift from traditional preventive maintenance to a prescriptive approach, aiming to extract knowledge on Track Circuits' status from data and support decision-making in the prioritization of maintenance interventions. • The main expected benefits of the approach include the early identification and correction of critical faults, mitigation of the risk of service disruptions, and efficient usage of track access times, ultimately increasing track availability. • The document also highlights the ongoing in-field tests and developments of the proposed architecture to improve the integration of data-driven analytics within the decision support system. • Additionally, the study plans to apply the approach to different rail signaling assets and introduce new decision-support functionalities in the follow-up project. • Overall, the conclusion underscores the potential of data-driven analytics and computational models in enabling proactive and smart maintenance planning, with the aim of optimizing resource utilization and minimizing service disruptions in railway infrastructure maintenance.