Predictive maintenance course for turbines
About our
The Predictive Turbine Maintenance Course provides comprehensive training in essential techniques for turbine condition inspection and analysis, using vibration analysis, thermography, oil analysis, and other advanced methodologies. The course focuses on early fault detection, maintenance scheduling optimization, and operating cost reduction through the application of specialized tools and software.
Participants gain hands-on experience in data interpretation, identifying anomalous patterns, and formulating recommendations for preserving turbine integrity and maximizing equipment availability. This program prepares professionals for roles such as predictive maintenance technicians, vibration analysts, and reliability engineers, with the goal of improving efficiency and safety in the energy and industrial sectors.
Predictive maintenance course for turbines
- Format: Hybrid
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 2
275 $
Competencies and results
What you will learn
1. Advanced Diagnostics and Performance Optimization of Turbines
- Comprehensive evaluation of critical components: blades, discs, and casings.
- Mastery of advanced techniques for fault diagnosis and vibration analysis.
- Implementation of strategies for optimizing performance and energy efficiency.
- Study of turbine lubrication, fuel, and control systems.
2. Predictive analysis of turbine failures and efficiency
- Identify and evaluate turbine failure mechanisms, including the analysis of flap-lag-torsion couplings.
- Understand and model the whirl flutter phenomenon for the prevention of catastrophic failures.
- Analyze and predict fatigue in turbine components, determining their service life and critical points.
- Apply finite element (FE) analysis techniques for the accurate dimensioning of laminates in composites.
- Design and evaluate structural and bonded joints using FE to ensure structural integrity.
- Implement damage tolerance strategies to mitigate the impact of defects and damage in turbines.
- Utilize non-destructive testing methods (NDT) such as UT/RT/thermography for early fault detection and quality control.
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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. Comprehensive Evaluation and Continuous Improvement of Turbines
- Diagnose and evaluate the condition of turbines, identifying faults and optimization opportunities.
- Apply continuous improvement methodologies to increase turbine efficiency and reliability.
- Implement performance evaluation techniques, including vibration analysis, thermography, and oil analysis.
5. Turbine Monitoring and Preservation Strategies
5. Turbine Monitoring and Preservation Strategies
- Understand and evaluate the complexities of flap-lag-torsion couplings, as well as whirl flutter phenomena and fatigue analysis in turbine components.
- Master the dimensioning and design of laminated structures in composites, including the analysis of joints and bonded joints using finite element (FE) methods.
- Apply advanced damage tolerance and non-destructive testing (NDT) techniques, including ultrasound (UT), radiography (RT), and thermography, for the early detection of failures and the extension of turbine service life.
6. Predictive Techniques and Integrity Assessment in Turbines
6. Predictive Techniques and Evaluation of Turbine Integrity
- 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).
Who this program is for:
Predictive maintenance course for turbines
- Graduates in Aerospace Engineering, Mechanical Engineering, Industrial Engineering, Automation Engineering, or related fields.
- Professionals in OEM rotorcraft/eVTOL, MRO, consulting, and technology centers.
- Flight Testing, certification, avionics, control, and dynamics seeking specialization.
- Regulators/authorities and UAM/eVTOL professionals requiring compliance skills.
Recommended requirements: foundation in aerodynamics and control and structures; ES/EN B2+/C1. We offer bridging tracks if you need them.
- 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 Fundamentals of Advanced Turbine Diagnostics: Principles and Technologies
1.2 Early Fault Detection Techniques: Data Analysis and Trends
1.3 Performance Optimization: Parameter Tuning and Performance Curves
1.4 Vibration Analysis: Interpretation and Diagnosis of Anomalies
1.5 Thermography: Identification of Hot Spots and Efficiency Losses
1.6 Non-Destructive Testing (NDT): Applications and Results in Turbines
1.7 Remote Monitoring and Data Acquisition Systems: Implementation and Analysis
1.8 Diagnostic Software and Tools: Use and Practical Applications
1.9 Case Studies: Diagnosis and Optimization in Different Types of Turbines
1.10 Continuous Improvement: Strategies for Sustainable Performance Optimization
2.2 Turbine Failure Modeling and Simulation
2.2 Data Analysis Techniques for Failure Prediction
2.3 Turbine Sensors and Monitoring Systems
2.4 Oil Analysis and Early Failure Detection
2.5 Root Cause Analysis (RCA) in Turbine Failures
2.6 Turbine Risk Assessment Methodologies
2.7 Predictive Analysis Software and Tools
2.8 Application of Artificial Intelligence in Failure Prediction
2.9 Predictive Failure Mitigation Strategies
2.20 Case Studies: Predictive Failure Analysis in Specific Turbines
3.3 Fundamentals of Advanced Turbine Diagnostics
3.2 Data and Signal Analysis Techniques
3.3 Combustion Optimization and Thermal Efficiency
3.4 Evaluation of Critical Components and Wear
3.5 Monitoring and Data Recording Systems
3.6 Simulation and Performance Modeling Tools
3.7 Interpretation of Results and Decision Making
3.8 Failure Analysis and Improvement Recommendations
3.9 Case Studies: Diagnosing Common Failures
3.30 Implementation of Optimization Strategies
2.3 Turbine Failure Modeling
2.2 Advanced Vibration Analysis Techniques
2.3 Thermography Applied to Turbines
2.4 Oil and Lubrication Analysis
2.5 Trend Analysis and Failure Forecasting
2.6 Energy Efficiency Analysis and Performance
2.7 Optimization of Operational Parameters
2.8 Failure Mitigation Strategies and Predictive Maintenance
2.9 Case Studies: Analysis of Common Failures and Solutions
2.30 Development of a Predictive Maintenance Program
3.3 Fundamentals of Turbine Vibrations
3.2 Vibration Analysis: Techniques and Equipment
3.3 Thermography: Principles and Applications in Turbines
3.4 Analysis of Spectra and Waveforms
3.5 Fault Detection Using Vibration and Thermography
3.6 Evaluation of Misalignments and Imbalances
3.7 Data Analysis and Report Generation
3.8 Case Studies: Problem Identification and Resolution
3.9 Application of Vibration Analysis Software
3.30 Implementation of a Vibration and Thermography Monitoring Program
4.3 Evaluation of the Current Turbine Condition
4.2 Root Cause Analysis Methodologies
4.3 Key Performance Indicators (KPIs)
4.4 Continuous Improvement Strategies (Lean, Six Sigma)
4.5 Performance and Efficiency Optimization
4.6 Cost-Benefit Analysis of Improvements
4.7 Change Implementation and Monitoring
4.8 Impact Assessment of Improvements
4.9 Case Studies: Implementation of Successful Improvements
4.30 Development of a Continuous Improvement Plan
5.3 Remote and Local Monitoring Systems
5.2 Sensors and Transducers in Turbines
5.3 Real-Time Data Analysis
5.4 Preservation and Protection Strategies
5.5 Lubrication and Cooling Systems
5.6 Corrosion and Erosion Protection
5.7 Preventive and Corrective Maintenance
5.8 Spare Parts Management Storage
5.9 Case Studies: Implementation of Monitoring and Preservation Strategies
5.30 Development of a Monitoring and Preservation Plan
6.3 Non-Destructive Testing (NDT) Techniques in Turbines
6.2 Remote Visual Inspection (RVI)
6.3 Ultrasonic Testing (UT) and Radiographic Testing (RT)
6.4 Oil Analysis and Lubrication
6.5 Structural Integrity Assessment
6.6 Fatigue and Remaining Life Analysis
6.7 Repair and Reinforcement Techniques
6.8 Development of an Inspection Plan
6.9 Case Studies: Application of Predictive Techniques
6.30 Estimation of Service Life and Maintenance Planning
7.3 Turbine Failure Mechanisms
7.2 Root Cause Analysis
7.3 Failure Mitigation Strategies
7.4 Risk and Failure Analysis
7.5 Condition-Based Maintenance (CBM)
7.6 Predictive Maintenance Programs
7.7 Failure Modeling and Simulation
7.8 Implementation of a Mitigation Plan
7.9 Case Studies: Failure Analysis and Solutions
7.30 Development of a Failure Mitigation Plan
8.3 Principles of Predictive Maintenance
8.2 Data and Trend Analysis
8.3 Optimization of Maintenance Intervals
8.4 Extending the Useful Life of Components
8.5 Condition-Based Maintenance (CBM) Strategies
8.6 Asset Management and Maintenance Scheduling
8.7 Life Cycle Cost Evaluation
8.8 Implementation of an Optimized Maintenance Program
8.9 Case Studies: Turbine Maintenance Optimization
8.30 Development of a Maintenance Optimization Plan
4.4 Performance and Efficiency Evaluation: Advanced methods for evaluating the overall performance of the turbine.
4.2 Data and Trend Analysis: Use of software for analyzing data collected from the turbines and detecting anomalous trends.
4.3 Operational Optimization: Operational adjustments and modifications to improve efficiency and reduce wear.
4.4 Critical Component Evaluation: Detailed review of key components, including bearings, blades, and seals.
4.5 Design for Reliability and Maintainability: Implementation of strategies to facilitate maintenance and extend service life.
4.6 Condition-Based Maintenance Strategies: Use of monitoring data to schedule preventive maintenance.
4.7 Cost-Benefit Analysis: Economic evaluation of implemented improvements.
4.8 Documentation and Procedures: Creation and updating of documentation and procedures to ensure consistency and efficiency. 4.9 Risk Identification and Mitigation: Assessment of the risks associated with turbine operation.
4.40 Implementation of a Continuous Improvement System: Establishment of a continuous improvement cycle to optimize performance.
5.5 Real-Time Turbine Monitoring Systems
5.5 Preservation and Corrosion Protection Methods
5.3 Oil and Lubricant Analysis for Optimizing Service Life
5.4 Fuel Filtration and Purification Strategies
5.5 Cooling Water Quality Control
5.6 Inspection and Maintenance of Critical Components
5.7 Alarm Management and Predictive Notifications
5.8 Optimized Start-up and Shutdown Procedures
5.9 Safe Storage and Handling of Spare Parts
5.50 Maintenance Documentation and Change Control
6.6 Early fault detection: Trend analysis and historical data.
6.2 Non-destructive testing (NDT) in turbines: Ultrasound, radiography, etc.
6.3 Oil and particle analysis: Key indicators of wear and contamination.
6.4 Advanced visual inspection: Borescopy and other remote inspection methods.
6.5 Vibration analysis: Spectrum interpretation and problem diagnosis.
6.6 Thermography: Identification of hot spots and insulation problems.
6.7 Structural integrity testing: Stress and strain analysis.
6.8 Remaining life assessment (RUL): Modeling and simulation.
6.9 Condition-based predictive maintenance planning.
6.60 Case studies: Practical application of predictive techniques.
- 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”).