Postgraduate Diploma in 2R Sensing and Data QA
Online 800-hour Postgraduate Diploma in 2R Sensing and Data QA. 8 modules covering JCGM 100:2008 (GUM), ISO/IEC 17025:2017 and MATLAB. 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 appropriate sensors for each quantity on a motorcycle.
Apply metrology principles and estimate measurement uncertainty.
Install and integrate sensors on the motorcycle's data bus.
Measure loads using calibrated strain gauges.
Design automatic data quality rules.
Process and fuse signals using digital and Kalman filters.
Validate measurement systems for development and competition testing.
Who it's for
- Instrumentation engineer looking to specialise in 2R Sensing and Data QA.
- Data acquisition technician looking to specialise in 2R Sensing and Data QA.
- Data quality engineer looking to specialise in 2R Sensing and Data QA.
- Vehicle test engineer looking to specialise in 2R Sensing and Data QA.
- Metrology technician looking to specialise in 2R Sensing and Data QA.
- Competition data engineer looking to specialise in 2R Sensing and Data QA.
- Engineering or science graduates looking to enter motorsport.
What you'll work with
- JCGM 100:2008 (GUM)
- ISO/IEC 17025:2017
- ISO/IEC 25012:2008
- ISO 16063-21:2003
- ISO 11898-1:2024
- ASAM MDF 4.2
- IEC 60584-1:2013
8 modules · 800 h
1.1Classification by quantity and physical principle1.2Inductive and Hall wheel-speed sensors1.3Linear and rotary potentiometers1.4Six-axis IMU units1.5Strain gauges on chassis and handlebars1.6Thermocouples, PT100 and infrared sensors1.7Brake and oil pressure sensors1.8Lambda probes and engine sensors1.9Tyre pressure sensors1.10Case study: sensor list for a Superbike
2.1Accuracy, precision and resolution2.2Calibration and metrological traceability2.3Measurement uncertainty according to the GUM2.4Signal conditioning and amplification2.5Analogue-to-digital conversion and resolution2.6Sampling theorem and aliasing2.7Anti-aliasing filter design2.8Noise, interference and shielding2.9Datasheets and interpretation2.10Laboratory: calibration of a suspension potentiometer
3.1Sensor location and mounting3.2Mounts and vibration isolation3.3Wiring, connectors and protection3.4Power supply and earthing3.5CAN bus integration3.6Channel configuration in the logger3.7Protection against water and heat3.8Weight and aerodynamics3.9Installation documentation3.10Practice: installing strain gauges on a swingarm
4.1Operating principle of the strain gauge4.2Wheatstone bridge configurations4.3Thermal and lead-wire compensation4.4Static calibration with known loads4.5Measuring forces on handlebars and footrests4.6Bending loads in fork legs4.7Stresses on swingarm and mountings4.8Load spectra for fatigue analysis4.9Correlation with finite element models4.10Laboratory: calibration of an instrumented seatpost
5.1Quality dimensions: accuracy, completeness and consistency5.2Detection of outliers and spurious spikes5.3Range saturation and clipping5.4Frozen channels and signal loss5.5Time offsets between sources5.6Cross-validation between redundant sensors5.7Automatic acceptance rules5.8Quality reports per session5.9Traceability between data, sensor and calibration5.10Workshop: automated validation script in Python
6.1Design of FIR and IIR digital filters6.2Numerical differentiation and noise amplification6.3Integration and cumulative drift6.4Spectral analysis using FFT6.5IMU and GNSS sensor fusion6.6Extended Kalman filter6.7Estimation of lean angle6.8Resampling and channel alignment6.9Lossless data compression6.10Practice: lean angle estimation using IMU-GPS fusion
7.1Bench testing of sensors7.2On-track verification against references7.3Validation plan for the complete system7.4Quantitative acceptance criteria7.5Instrumentation maintenance7.6Recalibration intervals7.7Recording and analysis of sensor failures7.8Documentation for audit7.9Evaluation of sensor suppliers7.10Case study: validation of a new pressure sensor
8.1Definition of the quantities to be measured8.2Selection of sensors and logger8.3Installation plan for the motorcycle8.4Calibration procedure8.5Data acquisition campaign8.6Data quality rules8.7Analysis of results8.8Uncertainty and reliability report8.9Dissertation 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 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 MATLAB, Python (NumPy, SciPy), MoTeC i2 Pro, HBK catman, AiM Race Studio, in free, academic or evaluation versions. Case studies cover real instrumentation procedures for a motorcycle with load and travel sensors, sensor calibration in the laboratory, and automated validation of track session data. Assessment is continuous, module by module, plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Select appropriate sensors for each quantity on a motorcycle.
Apply metrology principles and estimate measurement uncertainty.
Install and integrate sensors on the motorcycle's data bus.
Measure loads using calibrated strain gauges.
Design automatic data quality rules.
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 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 800 hours, spread over 6 to 8 months. That is an average of 10 to 15 hours per week, compatible with full-time work.
Students work with MATLAB, Python (NumPy, SciPy), MoTeC i2 Pro, HBK catman and AiM Race Studio. Free, academic or evaluation versions are used, and the tutor will indicate a free alternative where one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the integrative project. It is an in-house qualification, not an official title from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area are credited towards the higher programme. Check the credit transfer policy when you enrol.
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