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Postgraduate DiplomaAutomotive & Motorsport Applications open

Postgraduate Diploma in Telemetry and Alerting Automation

Online 800-hour postgraduate diploma in Telemetry and Alerting Automation. 8 modules covering IEC 62682, ANSI/ISA-18.2 and Python. Dedicated tutor and open enrolment.

Duration800 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
Enrol now View syllabus Ask an advisor
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Learning outcomes

What you will be able to do

01

Identify automation opportunities in telemetry.

02

Process sessions automatically.

03

Design effective alerting systems.

04

Implement real-time alerts.

05

Apply predictive failure models.

06

Operate automated systems reliably.

07

Integrate automated tools into the team.

Profile

Who it's for

  • Telemetry engineer wishing to specialise in Telemetry and Alerting Automation.
  • Data automation engineer wishing to specialise in Telemetry and Alerting Automation.
  • Reliability engineer wishing to specialise in Telemetry and Alerting Automation.
  • Data scientist wishing to specialise in Telemetry and Alerting Automation.
  • Motorsport software developer wishing to specialise in Telemetry and Alerting Automation.
  • Operations engineer wishing to specialise in Telemetry and Alerting Automation.
  • Engineering or science graduates seeking to enter motorsport.
Standards & tools

What you'll work with

  • IEC 62682
  • ANSI/ISA-18.2
  • ISO/IEC 27001:2022
  • ISO/IEC 20000-1
  • ISO 22301
  • ISO 8601-1
  • ASAM MDF 4.2
Syllabus

8 modules · 800 h

  1. 1.1Manual versus automated workflows
  2. 1.2Repetitive tasks in analysis
  3. 1.3Scripting in telemetry tools
  4. 1.4APIs of data tools
  5. 1.5Automation with Python
  6. 1.6Task scheduling
  7. 1.7Integration with team systems
  8. 1.8Script maintainability
  9. 1.9Dependency management
  10. 1.10Case study: automating a lap report
  1. 2.1Automatic detection of laps and outlaps
  2. 2.2Classification of valid laps
  3. 2.3Calculation of standard metrics
  4. 2.4Automatic comparison against reference
  5. 2.5Generation of derived channels
  6. 2.6Detection of track events
  7. 2.7Parallel processing
  8. 2.8Processing log
  9. 2.9Validation of results
  10. 2.10Laboratory: automatic processing of a session
  1. 3.1Objectives of an alerting system
  2. 3.2Reliability and performance alerts
  3. 3.3Static and dynamic thresholds
  4. 3.4Severity and prioritisation
  5. 3.5Alert fatigue and its prevention
  6. 3.6Grouping and suppression
  7. 3.7Alarm management per IEC 62682
  8. 3.8Notification channels
  9. 3.9Alert documentation
  10. 3.10Practice: designing an alert catalogue
  1. 4.1Stream processing for alerts
  2. 4.2Rules engines
  3. 4.3Model-based alerts
  4. 4.4Detection latency
  5. 4.5Alerts to the track engineer
  6. 4.6Vehicle safety alerts
  7. 4.7Integration with radio and displays
  8. 4.8Alert testing in simulation
  9. 4.9Alert logging
  10. 4.10Workshop: alert rules with Kafka Streams
  1. 5.1Predictive failure models
  2. 5.2Predictive maintenance of components
  3. 5.3Multivariate anomaly detection
  4. 5.4Edge machine learning
  5. 5.5Explainability of predictions
  6. 5.6Model retraining
  7. 5.7Evaluation with historical data
  8. 5.8False negative risks
  9. 5.9Model governance
  10. 5.10Case study: predicting fuel pump failure
  1. 6.1Monitoring of automation
  2. 6.2Incident management
  3. 6.3Response times
  4. 6.4Escalation procedures
  5. 6.5Testing before events
  6. 6.6System redundancy
  7. 6.7Recovery from failures
  8. 6.8Post-event incident review
  9. 6.9Service indicators
  10. 6.10Case study: alerting system failure during a race
  1. 7.1User requirements
  2. 7.2Training engineers
  3. 7.3Tool documentation
  4. 7.4Communicating changes
  5. 7.5User feedback
  6. 7.6Backlog management
  7. 7.7Prioritising development
  8. 7.8Measuring impact
  9. 7.9Automation culture
  10. 7.10Practice: tool deployment plan
  1. 8.1Analysis of current workflows
  2. 8.2Automation design
  3. 8.3Automatic session processing
  4. 8.4Alert catalogue
  5. 8.5Real-time implementation
  6. 8.6Development of the predictive model
  7. 8.7Simulation testing
  8. 8.8Impact assessment
  9. 8.9Report and presentation
  10. 8.10Final review with the tutor
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded lectures and a dedicated technical tutor. No fixed timetable: 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, Apache Kafka, Grafana, Prometheus, McLaren ATLAS, in free, academic or evaluation versions. Practical work covers real post-session analysis procedures, alert testing in race simulation, and evaluation of predictive models using historical data. Assessment is continuous per module, plus the final integrating project.

Accredited laboratories(EN ISO/IEC 17025) with a rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-driven final project:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:lecturers with a track record in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:a safety-by-design approach, with cyber-OT, DIH and compliance as its pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Analysis of current workflows

Identify automation opportunities in telemetry.

Automation design

Process sessions automatically.

Automatic session processing

Design effective alerting systems.

Alert catalogue

Implement real-time alerts.

Real-time implementation

Apply predictive failure models.

Career opportunities

Where this takes you

Telemetry engineerData automation engineerReliability engineerData scientistMotorsport software developerOperations engineer
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. No university degree is required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient level of 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.

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FAQ

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 a full-time job.

You work with Python, Apache Kafka, Grafana, Prometheus and McLaren ATLAS. Free, academic or evaluation versions are used, and the tutor indicates a free alternative where one exists.

A SEIUM own diploma upon passing continuous assessment and the integrating project. It is in-house training, not an official title from the Ministry.

Yes. Modules passed in a course or diploma in the same area are recognised in the advanced programme. Check the recognition policy when you enrol.

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