Diploma in Vibrations/Thermal and Health Index
Online postgraduate diploma of 800 hours in vibration/thermal monitoring and Health Index. 8 modules covering ISO 17359:2018, ISO 20816-1 and ISO 18434-1, with Python (SciPy and scikit-learn), MATLAB Predictive Maintenance Toolbox and FLIR Thermal Studio, with an assigned technical tutor.
Diploma in Vibrations/Thermal and Health Index — studying from Singapore
Singapore is a leading robotics and automation centre, with strong activity in logistics automation, industrial robotics and smart manufacturing. The Agency for Science, Technology and Research and Nanyang Technological University support robotics research, while the Economic Development Board promotes automation adoption. Engineers work on control systems, industrial automation, mobile robots and warehouse systems. Employers include multinational automation suppliers, logistics operators and manufacturers modernising their plants.
Fees for Singapore: S$2,130 (Singapore dollar), with instalments and employer sponsorship available.
Relevant bodies in Singapore
Typical roles
Questions from Singapore
Will this qualification be recognised by professional bodies in Singapore?
SEIUM's qualifications are private, not official state degrees, and are not accredited by any national body in Singapore. Engineers in Singapore can seek Professional Engineer status through the Professional Engineers Board, and the Institution of Engineers Singapore is a professional body for engineers. Recognition of a private qualification is decided by employers or the relevant body.
How might this training be used by employers in Singapore?
Singapore's manufacturing, logistics, electronics and smart-facility sectors use automation and control systems extensively, so these skills may suit roles in systems integration, controls engineering or automation support. The programme does not guarantee employment. Employers will usually look at your practical projects and experience alongside the qualification.
Can I study this alongside a full-time job, and can my employer help fund it?
Study is entirely online and in English, with an assigned technical tutor, so you can fit it around a job in Singapore. Master's programmes take 1,900 hours, postgraduate diplomas 800 hours and specialist courses 300 hours. Fees can be paid in instalments, and employer sponsorship is possible; automation teams in many firms may support relevant training.
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
Identify robot failure modes and how they show up in vibration and temperature.
Acquire vibration signals correctly during non-stationary motion.
Carry out thermographic inspections using standardised criteria.
Extract condition indicators normalised for load and speed.
Build robust, calibrated health indices.
Estimate remaining useful life and translate it into maintenance decisions.
Specify the architecture of a condition monitoring system.
Who it's for
- Predictive maintenance engineers moving towards data-driven health indices.
- Vibration analysts who want to add thermography and prognostics to their toolkit.
- Industrial thermographers looking to combine thermal data with vibration analysis.
- Reliability engineers responsible for critical rotating assets.
- Industrial data scientists working with condition-monitoring data.
- Maintenance managers of robotic cells.
- Engineering or science graduates aiming to enter the automotive sector.
What you'll work with
- ISO 17359:2018 (condition monitoring: general guidelines)
- ISO 20816-1 (measurement and evaluation of machine vibration)
- ISO 18434-1 (thermography for condition monitoring)
- ISO 13374 (condition data processing and communication)
- ISO 18436-7 (personnel qualification in thermography)
- ISO 13381-1 (prognostics: general guidelines)
- IEC 61131-9 (IO-Link)
- Python with SciPy and scikit-learn
- MATLAB Predictive Maintenance Toolbox
- FLIR Thermal Studio
- InfluxDB and Grafana
- Siemens MindSphere / Insights Hub
8 modules
1.1Failure modes in industrial robots1.2P-F curve and inspection interval1.3Condition-based maintenance according to ISO 173591.4Condition variables: vibration, temperature and current1.5Internal robot controller logs1.6Duty cycles and load profiles1.7Ageing of bearings and lubricants1.8Healthy-state baseline data1.9Asset criticality in a robotic cell1.10Workshop: criticality matrix for a robotic line
2.1Quantities: displacement, velocity and acceleration2.2Piezoelectric and MEMS accelerometers2.3Sensor mounting and frequency response2.4Sampling, aliasing and spectral resolution2.5Severity according to ISO 208162.6Vibration in axes with non-stationary motion2.7Repeatable test cycles on robots2.8Wireless acquisition and power supply2.9Signal quality and noise2.10Lab: vibration acquisition on a robot axis
3.1Principles of infrared radiation3.2Emissivity, reflection and windows3.3Thermal imaging cameras and resolution3.4Thermography of motors and electrical cabinets3.5Temperature sensors embedded in motors3.6Thermal models of motors and gearboxes3.7Thermal severity criteria3.8Thermographer qualification according to ISO 18436-73.9Writing the thermographic report3.10Practical: thermographic inspection of a drives cabinet
4.1Time-domain features: RMS, kurtosis and crest factor4.2Band-based spectral features4.3Thermal features: gradients and relative temperature4.4Motor current as a torque indicator4.5Normalisation by speed and load4.6Segmentation by duty cycle4.7Feature selection and reduction4.8Trend detection and regime changes4.9Statistical thresholds and alarms4.10Lab: feature pipeline in Python
5.1The health index concept and its scale5.2Weighted aggregation of features5.3Mahalanobis distance-based methods5.4Principal component analysis for health assessment5.5Fuzzy logic to combine evidence5.6Calibration with failure history5.7Monotonicity, robustness and sensitivity5.8Index uncertainty5.9Visualisation for maintenance5.10Practical: Health Index for a robot fleet
6.1Remaining useful life (RUL)6.2Exponential degradation models6.3Particle filters for prognosis6.4Survival and Weibull models6.5Turning the index into maintenance actions6.6Condition-based shutdown planning6.7Cost of false positives and false negatives6.8Integration with the CMMS6.9Predictive programme KPIs6.10Case study: gearbox replacement decision
7.1Sensor, edge and cloud architecture7.2IO-Link, OPC UA and MQTT protocols7.3Time-series storage7.4Condition dashboards7.5Alarm management and escalation7.6Cybersecurity of condition data7.7Training the maintenance team7.8Return on the predictive programme7.9Continuous improvement of the models7.10Workshop: specification of a monitoring system
8.1Selection of assets and failure modes8.2Measurement plan8.3Data acquisition8.4Feature extraction8.5Building the health index8.6Validation against historical events8.7Proposal for integration into maintenance8.8Economic analysis of the predictive programme8.9Report and presentation8.10Final project: final review with the tutor
How you'll learn
Training is 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python with SciPy and scikit-learn, MATLAB Predictive Maintenance Toolbox, FLIR Thermal Studio, InfluxDB and Grafana, and Siemens MindSphere / Insights Hub, in free, academic or evaluation versions. Testing is studied through real procedures: repeatable test cycles to measure vibration on robot axes, thermographic inspections of motors and electrical cabinets, and validation of the health index against failure history. Assessment is continuous by module, plus the final project.
Standards covered: ISO 17359:2018, ISO 20816-1, ISO 18434-1, ISO 13374, ISO 18436-7, ISO 13381-1 and IEC 61131-9 (IO-Link).
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
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.
- English sufficient to read technical standards.
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 over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with Python with SciPy and scikit-learn, MATLAB Predictive Maintenance Toolbox, FLIR Thermal Studio, InfluxDB and Grafana, and Siemens MindSphere / Insights Hub, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
Prior technical training or professional experience in the sector (no university degree required), a computer able to run the software, and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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