Postgraduate Diploma in Edge Analytics for Predictive Maintenance
Online 800-hour Postgraduate Diploma in Edge Analytics for Predictive Maintenance. 8 modules with ISO 13374-1, ISO 17359:2018 and Eclipse Kuksa. Assigned tutor and open enrolment.
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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
Design predictive maintenance programmes for vehicles.
Architect data analysis at the vehicle edge.
Pre-process signals and extract relevant features.
Build anomaly detection models.
Estimate remaining useful life with uncertainty.
Deploy optimised models on embedded hardware.
Integrate predictions into maintenance operations.
Who it's for
- Predictive maintenance engineer who wants to specialise in Edge Analytics for Predictive Maintenance.
- Fleet data scientist who wants to specialise in Edge Analytics for Predictive Maintenance.
- Edge computing engineer who wants to specialise in Edge Analytics for Predictive Maintenance.
- Fleet maintenance manager who wants to specialise in Edge Analytics for Predictive Maintenance.
- Reliability engineer who wants to specialise in Edge Analytics for Predictive Maintenance.
- Embedded software engineer who wants to specialise in Edge Analytics for Predictive Maintenance.
- Engineering or science graduates looking to enter fleet operators.
What you'll work with
- ISO 13374-1
- ISO 17359:2018
- ISO 13381-1:2015
- ISO 55001:2024
- Reglamento (UE) 2023/2854
- Reglamento (UE) 2016/679
- IEC 62443-4-2
- ISO 26262:2018
8 modules · 800 h
1.1Corrective, preventive and predictive maintenance1.2Condition-based maintenance according to ISO 173591.3ISO 13374 architecture for monitoring1.4Diagnosis versus prognosis1.5Remaining useful life (RUL)1.6Candidate components: battery, brakes, tyres and engine1.7Benefits for fleets and manufacturers1.8Return on investment of predictive maintenance1.9Success stories in heavy transport1.10Case study: prioritising components for a predictive programme
2.1Edge, fog and cloud in mobility2.2Computing resources in ECUs and telematics units2.3Embedded operating systems and containers2.4Eclipse Kuksa and COVESA VSS signals2.5Stream processing in the vehicle2.6Trade-off between local computing and transmission2.7Energy consumption of embedded analytics2.8Remote updating of analytical models2.9Security of workloads at the edge2.10Laboratory: analytics service with Kuksa on Raspberry Pi
3.1CAN signals, sensors and logs3.2Sampling frequency and anti-aliasing3.3Digital filtering and noise removal3.4Windowing and segmentation by operating conditions3.5Time-domain feature extraction3.6Spectral and envelope analysis3.7Wavelet transform for transients3.8Normalisation by load, temperature and speed3.9Feature compression for transmission3.10Practical: vibration features of a wheel bearing
4.1Statistical methods and control charts4.2Isolation forests4.3Autoencoders for time series4.4One-class models4.5Physics-based and residual models4.6Adaptive thresholds4.7Managing false alarms4.8Explainability of anomalies4.9Evaluation without abundant labels4.10Workshop: anomaly detector on brake data
5.1Degradation and reliability models5.2Weibull distribution and survival analysis5.3Particle filters for prognostics5.4LSTM networks and transformers for RUL5.5Guidelines of ISO 13381-15.6Prognostic uncertainty5.7State of health of traction batteries5.8Validation with failure histories5.9Prognostic metrics5.10Laboratory: RUL estimation with the C-MAPSS dataset
6.1Model quantisation and pruning6.2TensorFlow Lite and ONNX Runtime6.3Microcontrollers and TinyML6.4Federated learning across vehicles6.5Model lifecycle management in a fleet6.6Field performance monitoring6.7Cloud synchronisation6.8Model security and signing6.9Cost per vehicle6.10Practical: deploying a quantised model on a microcontroller
7.1From alert to work order7.2Integration with maintenance management systems7.3Workshop and spare parts planning7.4Asset management according to ISO 550017.5Alerts to the driver and fleet manager7.6Availability and cost indicators7.7Warranties and field quality7.8Data protection and the European Data Act7.9Continuous improvement of the programme7.10Case study: predictive programme for a bus fleet
8.1Selection of the component and the fleet8.2Data acquisition plan8.3Feature extraction8.4Anomaly model8.5Prognostic model8.6Deployment at the edge8.7Integration with maintenance8.8Economic analysis8.9Report and presentation8.10Final 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 timetables: students progress at their own pace within the enrolment period, 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 Eclipse Kuksa, TensorFlow Lite, AWS IoT Greengrass, Apache Kafka, Python with scikit-learn, in free, academic or evaluation versions. Tests are studied using real procedures for anomaly detection on fleet data, estimation of the remaining useful life of components, and deployment of models on embedded hardware. Assessment is continuous, module 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.
Design predictive maintenance programmes for vehicles.
Architect data analysis at the vehicle edge.
Pre-process signals and extract relevant features.
Build anomaly detection models.
Estimate remaining useful life with uncertainty.
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 level of Spanish to follow technical regulatory texts.
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 800 hours, spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with full-time work.
The programme uses Eclipse Kuksa, TensorFlow Lite, AWS IoT Greengrass, Apache Kafka and Python with scikit-learn. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
An SEIUM diploma awarded upon passing the continuous assessment and the integrating project. This 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 recognised towards the advanced programme. Check the recognition policy when you enrol.
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