Predictive maintenance course in production
Online course of 300 hours in predictive maintenance in production, structured in 6 modules covering maintenance strategies, condition monitoring, data analytics, shop-floor implementation and asset management, with an assigned technical tutor.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Compare maintenance strategies and how they apply to each asset.
Apply condition monitoring techniques to equipment.
Develop anomaly detection and remaining useful life models.
Integrate predictive maintenance with the CMMS.
Analyse failure modes using FMECA.
Manage assets in accordance with ISO 55000.
Design a well-justified predictive maintenance programme.
Who it's for
- Predictive maintenance engineers looking to strengthen their production expertise.
- Vibration analysis technicians moving towards data-driven diagnostics.
- Industrial maintenance managers modernising their maintenance strategy.
- Industrial data scientists applying analytics to equipment health.
- Reliability engineers working on critical production assets.
- Asset managers responsible for life-cycle and availability.
- Engineering or science graduates seeking to enter aircraft manufacturing.
What you'll work with
- ISO 17359:2018 (condition monitoring and diagnostics of machines, general guidelines)
- ISO 13374-1:2003 (data processing, communication and presentation for condition monitoring)
- ISO 55001:2024 (asset management system requirements)
- ISO 20816-3:2022 (measurement and evaluation of machine vibration)
- UNE-EN 13306:2018 (maintenance terminology)
- SAE JA1011 (evaluation criteria for RCM processes)
- IEC 60812:2018 (failure modes and effects analysis, FMEA and FMECA)
- ISO 18434-1:2008 (thermography for condition monitoring)
- MATLAB Predictive Maintenance Toolbox
- Python with scikit-learn
- IBM Maximo
- SKF Enlight
- Azure IoT
6 modules
1.1Corrective, preventive and predictive maintenance1.2Condition-based maintenance1.3P-F curve and early detection1.4Reliability-centred maintenance (RCM)1.5Equipment criticality analysis1.6MTBF, MTTR and availability indicators1.7Asset life-cycle costs1.8Critical equipment in aerospace manufacturing1.9Maintenance 4.0 strategy1.10Case study: criticality analysis of a machining shop
2.1Vibration analysis2.2Infrared thermography on electrical equipment2.3Oil and lubricant analysis2.4Airborne ultrasound2.5Motor current analysis2.6Monitoring of CNC machine spindles2.7Tool wear measurement2.8IIoT sensors and connectivity2.9Monitoring of autoclaves and furnaces2.10Workshop: diagnosing a bearing through vibration analysis
3.1Data acquisition and pre-processing3.2Signal feature extraction3.3Anomaly detection3.4Remaining useful life (RUL) models3.5Supervised machine learning3.6Physics-based versus data-driven models3.7Digital twins of assets3.8Data quality and labelling3.9Validation of predictive models3.10Practical exercise: anomaly detection model in Python
4.1Edge and cloud data architecture4.2Integration with CMMS and ERP4.3Alerts and workflows4.4Prioritisation of work orders4.5Predictive spare-parts management4.6Cybersecurity in industrial sensors4.7Training of maintenance staff4.8Programme value indicators4.9Scaling up pilots4.10Case study: deployment plan for a production line
5.1Asset management according to ISO 550005.2Failure analysis with FMECA5.3Root cause analysis of breakdowns5.4Improving equipment reliability5.5TPM and autonomous maintenance5.6Outcome-based maintenance contracts5.7Sustainability and energy efficiency5.8Audit of the maintenance system5.9Predictive maintenance maturity5.10Lab: FMECA of a machining centre
6.1Selection of the critical asset6.2Failure mode analysis6.3Selection of sensors and techniques6.4Proposed predictive model6.5Integration with maintenance management6.6Cost-benefit analysis6.7Data and sensor architecture6.8Pilot plan and scale-up6.9Report and presentation6.10Final project: final review with the tutor
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, making it compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB Predictive Maintenance Toolbox, Python with scikit-learn, IBM Maximo, SKF Enlight and Azure IoT, in free, academic or evaluation versions. Practical work is based on real procedures for equipment diagnosis through vibration analysis, development of predictive models with real data and sensor deployment plans on the shop floor. Assessment is continuous per module plus the final project.
Standards covered: ISO 17359:2018, ISO 13374-1:2003, ISO 55001:2024, ISO 20816-3:2022, UNE-EN 13306:2018, SAE JA1011, IEC 60812:2018, ISO 18434-1:2008.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- No prior qualification is required.
- An internet connection and a computer capable of running the programme software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 300 hours over 3 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with MATLAB Predictive Maintenance Toolbox, Python with scikit-learn, IBM Maximo, SKF Enlight and Azure IoT, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute certificate, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
No prior qualification is required: you need an internet connection, a computer capable of running the programme software and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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