Course in Predictive Analytics for Off-Road Fleets
300-hour online course in predictive analytics for Off-Road fleets. 6 modules covering ISO 13381-1 (prognostics in condition monitoring), ISO 13374 (condition monitoring data processing) 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
Explain the value of predictive analytics in off-road fleets.
Design the ingestion of telemetry data and ensure its quality.
Apply anomaly detection and diagnostic techniques.
Build failure prediction and remaining useful life models.
Turn alerts into work orders and maintenance decisions.
Establish a data and model governance framework in line with regulations.
Develop a complete predictive use case as a final project.
Who it's for
- Industrial data scientist looking to specialise in predictive analytics in Off-Road fleets.
- Reliability engineer looking to specialise in predictive analytics in Off-Road fleets.
- Fleet telematics analyst looking to specialise in predictive analytics in Off-Road fleets.
- Predictive maintenance manager looking to specialise in predictive analytics in Off-Road fleets.
- Data engineer looking to specialise in predictive analytics in Off-Road fleets.
- Machinery fleet manager looking to specialise in predictive analytics in Off-Road fleets.
- Engineering or science graduates looking to enter the mining sector.
What you'll work with
- ISO 13381-1 (prognostics in condition monitoring)
- ISO 13374 (condition monitoring data processing)
- ISO 17359 (condition monitoring, general guidance)
- ISO 14224 (reliability and maintenance data)
- ISO 15143-3 (telematic data exchange for machinery)
- Regulation (EU) 2024/1689 (artificial intelligence)
- Regulation (EU) 2023/2854 (Data Act)
6 modules · 300 h
1.1From corrective to predictive maintenance1.2Which decisions prediction can improve1.3Construction, mining, agricultural and forestry fleets: differences1.4Downtime cost as the objective function1.5Available data: telemetry, workshop and operations1.6Data maturity and minimum requirements1.7Realistic use cases and oversold ones1.8Business metrics versus model metrics1.9Team and required profiles1.10Workshop: prioritising use cases for a fleet
2.1Machine bus signals and their sampling2.2Fault codes and their interpretation2.3Workshop data: orders, spare parts and hours2.4Operational data: hours, loads and environment2.5Batch and streaming ingestion2.6Time-series storage2.7Gaps, duplicates and desynchronised clocks2.8Failure labelling and its quality2.9Data traceability and governance2.10Practice: building a fleet dataset
3.1Fixed thresholds versus adaptive thresholds3.2Univariate and multivariate anomaly detection3.3Reconstruction and density models3.4Vibration and temperature analysis3.5Sensor drift versus real degradation3.6Clustering of failure patterns3.7Assisted diagnosis and decision trees3.8Reducing false alarms3.9Validation with workshop personnel3.10Practice: anomaly detector on real signals
4.1Problem formulation: window and horizon4.2Failure classification models within a window4.3Survival and hazard models4.4Remaining useful life estimation4.5Censored data and its treatment4.6Temporal validation and information leakage4.7Calibration and uncertainty of predictions4.8Comparison with the current maintenance plan4.9Decision thresholds and error costs4.10Practice: failure prediction model for a component
5.1Integration with the maintenance management system5.2Generating work orders from the model5.3Prioritisation by criticality and availability5.4Planning shutdowns and spare parts5.5Advance parts ordering5.6Workshop feedback to the model5.7Measuring attributable savings5.8Change management with teams5.9Predictive programme indicators5.10Practice: designing the alert-to-work-order workflow
6.1Production deployment and model monitoring6.2Data drift and retraining6.3Version control and reproducibility6.4Platform costs and optimisation6.5Manufacturer data: access and dependency6.6Protection of operator data6.7System documentation and audit6.8Analytical maturity roadmap6.9Common errors in predictive projects6.10Final project: complete predictive programme for a fleet
How you'll learn
Fully 100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetable: students progress at their own pace within the intake, making it possible to combine the programme with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (pandas, scikit-learn), Power BI, Azure Machine Learning, Grafana, Apache Kafka, in free, academic or evaluation versions. Tests are studied using real procedures for remaining useful life modelling with telemetry data, anomaly detection in time series, and the design of maintenance dashboards. Assessment is continuous per 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.
Explain the value of predictive analytics in off-road fleets.
Design the ingestion of telemetry data and ensure its quality.
Apply anomaly detection and diagnostic techniques.
Build failure prediction and remaining useful life models.
Turn alerts into work orders and maintenance decisions.
Where this takes you
Requirements
- No prior degree is 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 totals 300 hours spread over 3 to 5 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), Power BI, Azure Machine Learning, Grafana and Apache Kafka. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
A SEIUM own diploma upon passing the continuous assessment and the integrating project. It is in-house training, not an official Ministry qualification.
Yes. Modules passed in a course or postgraduate diploma in the same area are credited towards the advanced programme. Check the credit transfer policy when you enrol.
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