Predictive maintenance course in production
About our
The Predictive Maintenance in Production course focuses on the application of advanced monitoring and data analysis techniques to predict equipment and machinery failures, thereby optimizing industrial maintenance and reducing downtime. It emphasizes the use of sensors, vibration analysis, thermography, and other predictive diagnostic tools, linking them to disciplines such as statistics, artificial intelligence, and Big Data within the context of Industry 4.0. The course aims to train participants in the implementation of predictive maintenance strategies, from data collection to the analysis and interpretation of results, in order to make informed decisions that improve operational efficiency. The program provides practical experience in the use of data analysis software and in the application of predictive models for the identification of failure patterns, in compliance with best practices in industrial safety and condition-based maintenance (CBM) standards. This training prepares professionals as predictive maintenance technicians, reliability engineers, maintenance data analysts, and production supervisors, boosting competitiveness in sectors such as manufacturing, energy, and petrochemicals.
Predictive maintenance course in production
- Format: Hybrid
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 3
249 $
Competencies and results
What you will learn
1. **Performance Prediction and Optimization of Productive Assets**
- Understand and apply advanced methods for predicting the performance of productive assets, including the detailed analysis of critical components.
- Study and model aeroelastic phenomena such as flap-lag-torsion, crucial for the structural integrity of aircraft and other structures.
- Analyze and mitigate risks associated with whirl flutter, a dynamic instability that can be catastrophic in rotating systems.
- Evaluate the useful life of assets by studying fatigue, using predictive models and tools.
- Design and dimension structures using composite materials, understanding their properties and applications in industry.
- Perform finite element (FE) analysis to evaluate the behavior of laminates, joints, and bonded joints, optimizing design and resistance.
Implement damage tolerance strategies, anticipating and managing potential damage to ensure the safety and durability of assets.
Master non-destructive testing (NDT) techniques, including UT (ultrasound), RT (radiography), and thermography, for the early detection of defects.
2. **Deep Mastery of Predictive Maintenance in Production Environments**
- Identification and diagnosis of failures in critical equipment, including vibration analysis, ultrasound, and thermography.
- Implementation of predictive maintenance strategies based on data analysis, trends, and probability models.
- Analysis of oils and lubricants, including interpretation of results and decision-making for optimizing equipment lifespan.
- Application of precision alignment techniques, dynamic balancing, and failure mode analysis.
- Proficiency in specialized software tools for predictive maintenance data analysis and asset management.
- Development of customized predictive maintenance programs for specific production environments.
- Evaluation of the efficiency and effectiveness of implemented predictive maintenance programs.
- Understanding of international regulations and standards related to predictive maintenance.
- Optimization of maintenance costs and equipment availability.
- Application of artificial intelligence and machine learning in predictive maintenance for continuous improvement.
and safety in production environments.
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. **Predictive Analysis for Continuous Improvement in Production**
4. **Predictive Analytics for Continuous Improvement in Production**
* Master the interpretation and application of predictive models to optimize production.
* Identify and evaluate key variables that impact efficiency and quality.
* Use statistical analysis tools to detect trends and patterns.
* Develop forecasting models for demand and resource planning.
* Apply simulation techniques to simulate scenarios and evaluate the impact of decisions.
* Implement real-time monitoring systems for production supervision and control.
* Analyze historical data to identify opportunities for continuous improvement.
* Integrate predictive analytics into strategic and operational decision-making.
* Evaluate process performance and establish key performance indicators (KPIs).
* Develop and implement improvement strategies based on predictive analytics.
* Optimize resource allocation and inventory management.
* Identify and mitigate production-related risks. * Collaborate with multidisciplinary teams to implement solutions.
* Communicate predictive analytics results effectively.
* Use specialized software for data analysis and predictive modeling.
5. **Implementation of Predictive Strategies for Production Efficiency**
5. **Implementation of Predictive Strategies for Production Efficiency**
Develop predictive models to optimize production processes.
Evaluate and apply risk analysis and production planning techniques.
Use simulation and data analysis tools for decision-making.
Implement predictive maintenance strategies to minimize downtime.
Analyze and optimize the supply chain to improve efficiency.
Apply Lean Manufacturing and Six Sigma principles for continuous improvement.
Manage and control production costs using key metrics.
Integrate information and communication systems for efficient production management.
Evaluate and select advanced production technologies to optimize processes.
Implement quality management and control strategies. of quality.
6. **Analysis and Prediction of Performance in Production Processes**
6. **Analysis and Prediction of Performance in Production Processes**
- Identify and evaluate the root causes of variations in production.
- Apply statistical analysis techniques to model and predict process behavior.
- Use simulation tools to optimize efficiency and reduce bottlenecks.
- Develop data-driven continuous improvement strategies.
- Implement real-time monitoring systems to track performance.
- Evaluate the impact of changes in production processes.
Who this program is for:
Predictive maintenance course in production
- Engineers with degrees in Aerospace Engineering, Mechanical Engineering, Industrial Engineering, or related fields.
- Technical personnel and professionals from OEM (Original Equipment Manufacturers) companies in sectors such as production and manufacturing.
- MRO (Maintenance, Repair, and Operations) specialists seeking to optimize predictive maintenance processes.
- Consultants from industrial sectors who wish to implement predictive maintenance solutions.
- Professionals from technology centers and research centers focused on the development of monitoring systems.
- Experts in areas such as Flight Testing, Product certification, avionics, process control, and dynamics engineering professionals interested in specializing in predictive maintenance.
Regulatory authority personnel and profiles related to Urban Air Mobility (UAM) who require knowledge of regulatory compliance.
Desired qualifications: Basic knowledge of data analysis and familiarity with production environments. A B2+/C1 level of Spanish is recommended. We offer bridging tracks for those who require 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 Introduction to Prediction and Optimization of Productive Assets
1.2 Data Analysis and Selection of Predictive Techniques
1.3 Performance Modeling and Asset Simulation
1.4 Condition-Based Maintenance (CBM) Optimization
1.5 Implementation of Sensors and Remote Monitoring
1.6 Failure Analysis and Design for Reliability
1.7 Spare Parts Inventory Optimization Strategies
1.8 Return on Investment (ROI) Evaluation of Assets
1.9 Case Studies: Practical Applications
1.10 Future Trends in Prediction and Optimization
2.2 Introduction to Predictive Maintenance: Key Concepts
2.2 Fundamentals of Predictive Maintenance Techniques
2.3 Sensors and Monitoring Technologies: An Overview
2.4 Vibration Analysis in Rotating Machinery
2.5 Infrared Thermography: Detection of Thermal Faults
2.6 Oil Analysis: Diagnosis and Trends
2.7 Ultrasound: Leak and Fault Detection
2.8 Programming and Planning Predictive Maintenance
2.9 Data Interpretation and Report Generation
2.20 Case Studies: Real-World Applications and Results
3.3 Fundamentals of Advanced Predictive Maintenance
3.2 Advanced Condition-Based Diagnostic Techniques
3.3 Vibration Analysis in Complex Systems
3.4 Infrared Thermography: Applications and Analysis
3.5 Ultrasound: Fault and Leak Detection
3.6 Predictive Lubrication: Analysis and Control
3.7 Oil Analysis: Interpretation of Results
3.8 Laser Alignment and Dynamic Balancing
3.9 Modeling and Simulation for Failure Prediction
3.30 Implementation and Optimization Strategies
4.4 Introduction to Predictive Analytics in Production
4.2 Data Collection and Analysis in Production Environments
4.3 Modeling and Simulation of Production Processes
4.4 Identification of Critical Variables and Their Impact
4.5 Failure Analysis Techniques and Predictive Diagnostics
4.6 Implementation of Predictive Models for Decision Making
4.7 Monitoring and Optimization of Production Assets
4.8 Integration of Predictive Analytics Systems
4.9 Case Studies: Real-World Applications in Industry
4.40 Evaluation and Continuous Improvement of Predictive Strategies
5.5 Introduction to Predictive Implementation in Production
5.5 Production Data Collection and Analysis
5.3 Selection of Appropriate Predictive Technologies
5.4 Design and Implementation of a Predictive System
5.5 Integration of the Predictive System with the Existing Infrastructure
5.6 Staff Training and Development
5.7 Monitoring and Tuning the Predictive System
5.8 Return on Investment (ROI) Evaluation
5.9 Continuous Improvement of the Predictive System
5.50 Case Studies of Successful Predictive Implementation
6.6 Introduction to Predictive Methodology in Production
6.2 Data Collection and Analysis for Prediction
6.3 Statistical Modeling and Time Series Analysis
6.4 Machine Learning Techniques Applied to Prediction
6.5 Identification and Analysis of Failures in Production Systems
6.6 Prediction of Equipment and Machinery Performance
6.7 Implementation of Predictive Key Performance Indicators (KPIs)
6.8 Validation and Verification of Predictive Models
6.9 Integration of Prediction into Decision Making
6.60 Practical Cases and Real-World Applications in Industry
- 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”).