Predictive Maintenance Course for Rolling Stock
About our
The Predictive Maintenance in Rolling Stock course focuses on the application of advanced techniques for monitoring and diagnosing faults in railway vehicles. Vibration analysis, thermography, and oil analysis tools are integrated for the early detection of problems in critical components such as motors, bearings, and braking systems. The course provides knowledge in sensors, data acquisition, and predictive modeling, improving reliability and reducing maintenance costs. The program includes hands-on experience in operating diagnostic equipment and analyzing data to develop predictive maintenance plans. It covers current regulations and best practices for implementing predictive maintenance strategies, training professionals capable of optimizing the performance and lifespan of rolling stock. Participants will learn to identify and troubleshoot faults before they cause unscheduled downtime.
Predictive Maintenance Course for Rolling Stock
- Format: Online
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 2
649 $
Competencies and results
What you will learn
1. Predictive Failure Analysis in Rolling Stock: Key Learnings
- Identify and understand rolling stock failure modes, including vibration and resonance analysis.
- Apply predictive analysis techniques to anticipate and prevent failures, optimizing component lifespan.
- Master the use of simulation and modeling tools for stress and strain analysis.
- Interpret data from sensors and monitoring systems to assess rolling stock condition in real time.
- Develop predictive maintenance strategies based on data analysis and pattern identification.
- Apply risk management methodologies to minimize the impact of failures on operations and safety.
1. Optimizing Rolling Stock Performance Through Failure Prediction
- Analyze flap–lag–torsion couplings, whirl flutter and fatigue.
- Size laminates in composites, unions and bonded joints with FE.
- Implement damage tolerance and NDT (UT/RT/thermography).
3. Comprehensive user-oriented design and validation (from modeling to manufacturing)
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
4. Mastering Failure Prediction in Railway Components: A Comprehensive Course
4. Mastering Failure Prediction in Railway Components: A Comprehensive Course
- Identify and evaluate failure modes in critical components.
- Apply fatigue analysis techniques for service life prediction.
- Use simulation tools for finite element analysis (FEA).
- Understand the principles of fracture mechanics and their application.
- Master vibration analysis and its relationship to failures.
- Interpret non-destructive testing (NDT) data for early failure detection.
- Implement predictive maintenance strategies based on data analysis.
- Analyze flap-lag-torsion, whirl flutter, and fatigue couplings.
- Dimension laminates in composites, unions and bonded joints with FE.
- Implement damage tolerance and NDT (UT/RT/thermography).
5. Maintenance Prediction in Railway Fleets: Advanced Techniques
5. Predictive Maintenance in Railway Fleets: Advanced Techniques
- Diagnosing failures in critical components using vibration and acoustic analysis.
- Applying Machine Learning techniques for predicting failures in railway systems.
- Managing railway infrastructure through condition monitoring and predictive analytics.
- Evaluating wheel and rail wear using simulation models and data analysis.
- Developing predictive maintenance strategies based on data analysis and resource optimization.
6. Prediction of Wear and Failure in Rolling Stock Components
6. Prediction of Wear and Failure in Rolling Stock Components
- Evaluation of failure modes in components, including friction, adhesive, and abrasive wear analysis.
- Application of Life Cycle Assessment (LCA) techniques for component optimization.
- Identification of corrosion mechanisms and protection strategies, such as coatings and surface treatments.
- Implementation of predictive models for crack propagation and remaining life calculation.
- Use of computational analysis (FEA) tools for stress and strain analysis in components subjected to dynamic loads.
- Proficiency in non-technical testing methodologies Destructive testing (NDT) methods such as ultrasound, radiography, and magnetic particle inspection for defect detection.
Study of the fatigue of materials under cyclic loads and the application of S-N curves.
Analysis of the reliability of components and systems, including the calculation of failure rate and mean time between failures (MTBF).
Understanding the factors that influence the performance and durability of rolling stock components.
Application of regulations and standards related to the safety and structural integrity of components.
Who this program is for:
Predictive Maintenance Course for Rolling Stock
- Engineers with degrees in Naval Engineering, Mechanical Engineering, Electrical Engineering, or related disciplines.
- Professionals from shipyards, naval rolling stock companies, maintenance companies, and naval operators.
- Technical predictive maintenance technicians and supervisors seeking to update their knowledge and skills.
- Staff from technical offices and R&D departments interested in implementing or improving predictive maintenance strategies.
Recommended qualifications: Basic knowledge of mechanics, electricity, and rolling stock systems; Understanding of data analysis concepts.
- Standards-driven curriculum: you will work with CS-27/CS-29, DO-160, DO-178C/DO-254, ARP4754A/ARP4761, ADS-33E-PRF from the first module.
- Accreditable laboratories (EN ISO/IEC 17025) with rotor bench, EMC/Lightning pre-compliance, HIL/SIL, vibrations/acoustics.
- Evidence-oriented TFM: safety case, test plan, compliance dossierand operational limits.
- Mentored by industry: teachers with experience in rotorcraft, tiltrotor, eVTOL/UAM and flight test.
- Flexible modality (hybrid/online), international cohorts and support from SEIUM Career Services.
- Ethics and security: safety-by-design approach, cyber-OT, DIH and compliance as pillars.
1.1 Importance of predictive analytics in the railway sector.
1.2 Benefits of failure prediction in rolling stock management.
1.3 Definition of key concepts: predictive analytics, predictive maintenance, and reliability.
1.4 Common types of rolling stock failures and their impact.
1.5 The rolling stock lifecycle and the importance of prediction.
1.6 Introduction to the technologies and tools used in predictive analytics.
1.7 Data collection and analysis: information sources.
1.8 Fundamentals of time series and their application in prediction.
1.9 Relevant standards and regulations in railway predictive analytics.
1.10 Case studies: examples of successful applications of predictive analytics.
2.2 Introduction to Rolling Stock Failure Prediction
2.2 Types of Failures: Mechanical, Electrical, and Electronic
2.3 Failure Models: Weibull, Exponential, etc.
2.4 Historical Data: Collection and Analysis
2.5 Sensors and Monitoring Systems
2.6 Predictive Algorithms: Machine Learning and AI
2.7 Key Performance Indicators (KPIs)
2.8 Root Cause Analysis (RCA)
2.9 Predictive Maintenance Strategies
2.20 Implementation and Challenges
3.3 Introduction to Failure Prediction in Rolling Stock
3.2 Fundamentals of Predictive Monitoring in Railway Components
3.3 Data Analysis Techniques for Failure Prediction
3.4 Sensors and Data Acquisition Systems in Rolling Stock
3.5 Failure Modeling and Trend Analysis
3.6 Failure Prediction Algorithms: Machine Learning and AI
3.7 Wear Prediction in Critical Components
3.8 Predictive Maintenance Strategies Based on Failure Prediction
3.9 Implementing a Failure Prediction Program
3.30 Case Studies: Real-World Applications and Results
4.4 Fundamentals of Failure Prediction in Railway Components
4.2 Data and Signal Analysis for Failure Prediction
4.3 Sensors and Monitoring Technologies in Rolling Stock
4.4 Predictive Modeling and Machine Learning Algorithms
4.5 Applications of Failure Prediction in Railway Systems
4.6 Predictive Maintenance Strategies
4.7 Integrating Prediction into Fleet Management
4.8 Case Studies: Failure Prediction in Different Components
4.9 Tools and Software for Failure Prediction
4.40 Implementation and Evaluation of Failure Prediction Programs
5.5 Introduction to Predictive Monitoring in Railways: Concepts and Benefits
5.5 Sensors and Data Acquisition Technologies for Rolling Stock
5.3 Data Analysis: Processing and Cleaning Techniques
5.4 Predictive Modeling: Algorithms and Machine Learning Methods
5.5 Failure Prediction in Key Components: Motors, Brakes, Wheels
5.6 Implementation of Monitoring Systems: Hardware and Software
5.7 Data and Systems Integration: Platforms and Architectures
5.8 Predictive Maintenance: Strategies and Action Plans
5.9 Case Studies: Practical Applications and Results
5.50 The Future of Predictive Monitoring in the Railway Sector
6.6 Modeling the Wear of Railway Components
6.2 Wear Data Analysis: Techniques and Tools
6.3 Predicting Wear on Wheels and Rails
6.4 Predicting Wear in Braking Systems
6.5 Predicting Wear in Suspension Components
6.6 Influence of Environmental and Operational Factors on Wear
6.7 Strategies for Mitigating Wear and Extending Service Life
6.8 Designing for Durability and Reducing Wear
6.9 Integrating Wear Prediction into the Maintenance Plan
6.60 Practical Examples and Case Studies on Wear Prediction
- Hands-on methodology: test-before-you-trust, design reviews, failure analysis, compliance evidence.
- Software (depending on licenses/partners): MATLAB/Simulink, Python (NumPy/SciPy), OpenVSP, SU2/OpenFOAM, Nastran/Abaqus, AMESim/Modelica, acoustics tools, planning toolchains DO-178C.
- SEIUM Laboratories: scale rotor bench, vibrations/acoustics, EMC/Lightning pre-compliance, HIL/SIL for AFCS, data acquisition with strain gauging.
- Standards and compliance: EN 9100, 17025, ISO 27001, GDPR.
Capstone-type projects
Admissions, fees, and scholarships
- Profile: Background in Computer Engineering, Mathematics, Statistics, or related fields; practical experience in NLP and valued information retrieval systems.
- Documentation: Updated CV, academic transcript, SOP/statement of purpose, project examples or code (optional).
- Process: Application → Technical evaluation of profile and experience → Technical interview → Review of case studies → Final decision → Enrollment.
- Fees:
- Single payment: 10% discount.
- Payment in 3 installments: No fees; 30% upon registration + 2 equal monthly payments of the remaining 35%.
Monthly payment: available with a 7% commission on the total; annual review.
Scholarships: based on academic merit, financial need, and promoting inclusion; agreements with companies in the sector for partial or full scholarships.
See “Calendar & Calls for Applications,” “Scholarships & Grants,” and “Fees & Financing” in the SEIUM mega-menu.
Do you have any questions?
Our team is ready to help you. Contact us, and we will respond as soon as possible.
F. A. Q
Frequently asked questions
Yes, we have international certification
Yes: experimental models, real data, applied simulations, professional environments, real case studies.
It is not mandatory. We offer leveling tracks and tutoring.
Completely. It covers e-propulsion, integration, and emerging regulations (SC-VTOL).
Recommended. There are also internal challenges and consortiums.
Yes. Online/hybrid modality with planned labs and visa support (see “Visa & Residence”).