Postgraduate Diploma in Data-Driven Predictive Maintenance
Online 800-hour Postgraduate Diploma in Data-Driven Predictive Maintenance. 8 modules covering ISO 17359 (condition monitoring: general guidelines), ISO 13374 (processing and exchange of monitoring data) and Python (pandas, scikit-learn). Assigned tutor and open enrolment.
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
Distinguish maintenance strategies and the role of predictive maintenance.
Design data acquisition that ensures data quality and traceability.
Apply signal processing techniques to extract condition indicators.
Implement anomaly detection algorithms on machine data.
Estimate remaining useful life using diagnostic and prognostic models.
Translate model results into maintenance decisions.
Plan the organisational implementation of a predictive programme.
Who it's for
- Predictive maintenance engineer who wants to specialise in Data-Driven Predictive Maintenance.
- Industrial data scientist who wants to specialise in Data-Driven Predictive Maintenance.
- Reliability engineer who wants to specialise in Data-Driven Predictive Maintenance.
- Asset management manager who wants to specialise in Data-Driven Predictive Maintenance.
- Condition monitoring analyst who wants to specialise in Data-Driven Predictive Maintenance.
- Digital transformation consultant for maintenance who wants to specialise in Data-Driven Predictive Maintenance.
- Engineering or science graduates seeking to enter the maritime transport sector.
What you'll work with
- ISO 17359 (condition monitoring: general guidelines)
- ISO 13374 (processing and exchange of monitoring data)
- ISO 13381-1 (prognostics of machine condition)
- EN 13306 (maintenance terminology)
- ISO 55001 (asset management)
- ISO 20816-1 (evaluation of machine vibration)
8 modules · 800 h
1.1From reactive to prescriptive maintenance1.2ISO 13374 standard for condition data processing1.3Prognostics and health management (PHM)1.4Degradation curves and failure modes1.5Candidate naval equipment: engines, pumps and generators1.6Operational data versus condition data1.7Health indicators and thresholds1.8Economic value of predictive maintenance1.9Digital maturity of maintenance1.10Practical: selecting critical equipment for predictive maintenance
2.1Vibration, temperature and pressure sensors2.2Data from ship automation systems2.3Data historians and sampling frequency2.4Synchronisation and timestamps2.5Missing data and outliers2.6Fault labelling and maintenance records2.7Ship-to-shore transmission and compression2.8Data governance and ownership2.9Edge and cloud architectures2.10Workshop: cleaning auxiliary engine data in Python
3.1Time-domain analysis3.2Fourier transform and spectra3.3Time-frequency analysis and wavelets3.4Filtering and noise removal3.5Extraction of statistical features3.6Normalisation by operating condition3.7Dimensionality reduction with PCA3.8Change detection in time series3.9Python libraries for signal processing3.10Practical: vibration features of a centrifugal pump
4.1Statistical models and process control4.2Isolation Forest and One-Class SVM4.3Autoencoders for anomaly detection4.4Regression-based normality models4.5Adaptive thresholds and false alarms4.6Evaluation without fault labels4.7Explainability of alarms4.8Integration with ship alarms4.9Cases on marine diesel engines4.10Laboratory: anomaly detector for exhaust temperatures
5.1Supervised fault classification5.2Tree models and gradient boosting5.3Recurrent neural networks and LSTM5.4Estimation of remaining useful life (RUL)5.5Survival models and Weibull5.6Hybrid physics-data models5.7Uncertainty quantification5.8C-MAPSS reference datasets5.9Temporal validation of models5.10Practical: estimating RUL with LSTM
6.1Prescriptive maintenance and recommendations6.2Optimising maintenance intervals6.3Integration with the planned maintenance system6.4Spare parts planning and logistics6.5Deployment and MLOps6.6Monitoring model performance6.7Acceptance by condition-based maintenance class6.8Platform cybersecurity6.9Return on investment6.10Case study: intervention decision for a generator
7.1Implementation roadmap7.2Multidisciplinary data teams7.3Change management with crews7.4Suppliers and service models7.5Availability-based contracts7.6Maintenance indicators7.7Lessons from fleet projects7.8Model ethics and transparency7.9Scaling up to the whole fleet7.10Case study: predictive maintenance pilot on three ships
8.1Equipment selection and use case8.2Dataset preparation8.3Feature extraction8.4Anomaly detection model8.5Prognostic model8.6Operational integration proposal8.7Economic evaluation of the use case8.8Deployment plan8.9Report and presentation8.10Final review with the tutor
How you'll learn
100% online training on the SEIUM Institute campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine study with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (pandas, scikit-learn), MATLAB Predictive Maintenance Toolbox, InfluxDB, Grafana, in free, academic or evaluation versions. Tests are studied using real procedures for processing real vibration signals, training anomaly detection models and estimating remaining useful life. Assessment is continuous across each module, plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Distinguish maintenance strategies and the role of predictive maintenance.
Design data acquisition that ensures data quality and traceability.
Apply signal processing techniques to extract condition indicators.
Implement anomaly detection algorithms on machine data.
Estimate remaining useful life using diagnostic and prognostic models.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. A university degree is not required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient Spanish to follow technical regulatory reading.
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 consists of 800 hours in total, spread over 6 to 8 months. That is an average of 10 to 15 hours per week, compatible with full-time work.
You will work with Python (pandas, scikit-learn), MATLAB Predictive Maintenance Toolbox, InfluxDB, Grafana. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM Institute diploma upon passing the continuous assessment and the integrating project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area are credited towards the higher programme. Check the recognition policy when you enrol.
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