Course in Predictive Maintenance with Big Data
Online 300-hour course in predictive maintenance with big data. 6 modules covering ISO 55001, ISO 17359 and Apache Spark. Assigned tutor and open enrolment.
Take the controls.
Run a real engineering trade-off from this programme. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Select maintenance strategies based on asset criticality.
Apply condition monitoring techniques to energy assets.
Design big data architectures for maintenance data.
Develop predictive models of failure and remaining useful life.
Validate models with failure histories and control false alarms.
Integrate predictions into maintenance planning.
Carry out a predictive maintenance project using real data.
Who it's for
- Reliability engineer looking to specialise in Predictive Maintenance with big data.
- Industrial data scientist looking to specialise in Predictive Maintenance with big data.
- Maintenance manager for energy assets looking to specialise in Predictive Maintenance with big data.
- Condition monitoring engineer looking to specialise in Predictive Maintenance with big data.
- Asset management analyst looking to specialise in Predictive Maintenance with big data.
- Industrial data architect looking to specialise in Predictive Maintenance with big data.
- Engineering or science graduates looking to enter renewable generation.
What you'll work with
- ISO 55001
- ISO 17359
- ISO 13374
- IEC 60599
- ISO 10816-21
- IEC 60812
- SAE JA1011
- EN 13306
6 modules · 300 h
1.1Corrective, preventive and predictive maintenance1.2Condition-based maintenance1.3Reliability-centred maintenance RCM1.4Failure mode, effects and criticality analysis FMECA1.5P-F curves and the detection window1.6MTBF, MTTR and availability indicators1.7Asset management according to ISO 550001.8Asset lifecycle costs1.9Criticality of energy assets1.10Workshop: FMECA of a power transformer
2.1Vibration analysis in wind turbines2.2Dissolved gas analysis in transformers2.3Partial discharges in cables and switchgear2.4Thermography of electrical equipment2.5Oil and particle analysis2.6Monitoring of bearings and gearboxes2.7Drone inspection and imaging2.8SCADA data from renewable plants2.9IoT sensors and data acquisition2.10Laboratory: transformer diagnosis using DGA
3.1Volume, velocity and variety of industrial data3.2Data lakes and lakehouses3.3Data ingestion with Kafka3.4Distributed processing with Spark3.5Time-series databases3.6Industrial historians and PI System3.7Cloud and edge computing3.8Data quality and contextualisation3.9Asset data governance3.10Practice: vibration data pipeline with Spark
4.1Anomaly detection using normal behaviour models4.2Estimation of remaining useful life RUL4.3Survival models and Weibull4.4LSTM networks for time series4.5Physical and hybrid models4.6Failure mode classification4.7Alarm thresholds and false positives4.8Validation with failure histories4.9Explainability of predictions4.10Case study: failure prediction in wind turbine gearboxes
5.1Integration with CMMS and work orders5.2Risk-based intervention planning5.3Spare parts management with prediction5.4Asset health dashboards5.5Model deployment and monitoring5.6Return on investment of predictive maintenance5.7Team organisation and competencies5.8Cybersecurity of operational data5.9Success stories in wind farms and substations5.10Case study: predictive maintenance plan for a substation
6.1Selection of the asset and failure mode6.2Criticality analysis6.3Definition of variables and sensors6.4Data architecture6.5Development of the predictive model6.6Validation and alarm thresholds6.7Integration with maintenance management6.8Economic evaluation6.9Report and presentation6.10Final 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. No fixed timetable: students progress at their own pace within the intake, which makes 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 Apache Spark, Apache Kafka, AVEVA PI System, Python (scikit-learn, TensorFlow), IBM Maximo, in free, academic or evaluation versions. Tests are studied using real transformer diagnostic procedures through dissolved gas analysis, failure prediction in wind turbine gearboxes, and the design of a condition data pipeline. Assessment is continuous by 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.
Select maintenance strategies based on asset criticality.
Apply condition monitoring techniques to energy assets.
Design big data architectures for maintenance data.
Develop predictive models of failure and remaining useful life.
Validate models with failure histories and control false alarms.
Where this takes you
Requirements
- No prior degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient command of Spanish to follow technical standards documentation.
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 a full-time job.
The programme works with Apache Spark, Apache Kafka, AVEVA PI System, Python (scikit-learn, TensorFlow) and IBM Maximo. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM 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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