Master’s in Predictive Maintenance and Condition Monitoring Engineering
Online master's programme of 1,900 hours in predictive maintenance and condition monitoring engineering, structured in 12 modules covering vibration analysis, oil analysis, infrared thermography, ultrasound, electrical monitoring, IoT sensors, machine learning, remaining useful life prognosis and digital twins under ISO 17359, ISO 13374 and ISO 20816-1, with an assigned technical tutor.
Master’s in Predictive Maintenance and Condition Monitoring Engineering — studying from Canada
Canada's off-road and heavy machinery sector serves mining, forestry, agriculture and construction, all major contributors to the national economy. Manufacturers and dealers in Ontario, Alberta, Saskatchewan and British Columbia supply equipment for resource extraction and infrastructure. Mining activity in Northern Ontario, Quebec and the territories drives demand for rugged vehicle engineering and maintenance expertise. Engineers work on hydraulics, drivetrains, machine safety and special vehicle design.
Fees for Canada: C$12,400 (Canadian dollar), with instalments and employer sponsorship available.
Relevant bodies in Canada
Typical roles
Questions from Canada
Is this programme recognised by professional bodies in Canada for off-road and special vehicle engineering?
SEIUM's qualifications are private, not official state degrees, and are not accredited nationally in Canada. Professional engineering titles are regulated provincially, and vehicle and equipment safety is governed by standards such as those from the CSA and rules from Transport Canada where applicable. Recognition of a private qualification is decided by employers or the relevant body.
What employers in Canada might value this qualification for special vehicles and machinery?
Employers could include mining, forestry, agricultural, construction and specialist vehicle manufacturers across Canada. Each employer decides how much weight to place on a private qualification, and practical experience is often central in these sectors. The final project can demonstrate applied capability, but it does not guarantee any role.
How can I study this while working in Canada, and how is it funded?
The programme is online and in English, so you can study from anywhere in Canada without a student visa. Choose from 300-hour specialist courses, 800-hour postgraduate diplomas or 1,900-hour master's programmes. Fees are payable in instalments, and employer sponsorship is possible, which some Canadian employers offer for job-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
Select predictive techniques according to failure modes.
Diagnose defects through vibration analysis.
Interpret oil analyses and thermographic surveys.
Deploy IoT sensors and data architectures.
Apply machine learning to anomaly detection.
Estimate the remaining useful life of components.
Implement predictive programmes with measurable returns.
Who it's for
- Vibration analysts.
- Predictive maintenance engineers.
- Industrial data scientists.
- Reliability engineers.
- Thermography technicians.
- Condition monitoring managers.
- Engineering or science graduates seeking to enter the mining sector.
What you'll work with
- ISO 17359 (condition monitoring: general guidelines)
- ISO 13374 (condition monitoring data processing)
- ISO 20816-1 (measurement and evaluation of vibration)
- ISO 18436-2 (qualification of vibration analysts)
- ISO 4406 (particle contamination code)
- ISO 18434-1 (thermography for condition monitoring)
- ISO 13381-1 (condition prognostics)
- Python with scikit-learn
- MATLAB Predictive Maintenance Toolbox
- Emerson AMS Machine Works
- Microsoft Azure IoT
12 modules · 1900 h
1.1P-F curve and detection interval1.2Condition-based maintenance1.3Available monitoring techniques1.4Selection of techniques by failure mode1.5Return on predictive investment1.6Condition monitoring standards1.7Organisation of a predictive programme1.8Analyst competencies1.9Common implementation errors1.10Case study: selecting techniques for an excavator
2.1Mechanical vibration in rotating machines2.2Transducers and their mounting2.3FFT spectral analysis2.4Unbalance, misalignment and looseness2.5Bearing defects2.6Envelope analysis2.7Gear defects2.8Vibration in variable-speed machines2.9Severity according to ISO 208162.10Lab: bearing diagnosis using envelope analysis
3.1Oil analysis programme3.2Elemental spectrometry3.3Particle counting to ISO 44063.4Ferrography and wear particles3.5Viscosity and acid number3.6Water and fuel in oil3.7Soot and oxidation3.8Limits and trends3.9Integration with vibration analysis3.10Practical exercise: interpreting trends for a diesel engine
4.1Infrared radiation and temperature4.2Thermal imaging cameras and their selection4.3Emissivity and correction factors4.4Thermography of electrical systems4.5Thermography of mechanical components4.6Thermography of hydraulic systems4.7Thermographic inspection routes4.8Severity criteria4.9Preparing thermographic reports4.10Lab: thermographic inspection of an electrical panel
5.1Airborne ultrasound inspection5.2Detection of air and gas leaks5.3Ultrasound-assisted lubrication5.4Detection of electrical discharges5.5Acoustic emission in structures5.6Detection of active cracks5.7Equipment and sensors5.8Measurement procedures5.9Interpretation of results5.10Practical exercise: ultrasound-guided bearing lubrication
6.1Motor current signature analysis6.2Rotor and stator defects6.3Power quality6.4Motor insulation6.5Battery monitoring6.6Power electronics in electric machines6.7Monitoring of diesel engines6.8Process pressures and temperatures6.9Combined diagnosis using several techniques6.10Case study: diagnosis of a traction motor
7.1Wireless vibration sensors7.2Temperature and pressure sensors7.3Industrial data gateways7.4MQTT and OPC UA protocols7.5Edge computing7.6Cloud platforms7.7Manufacturer telematics7.8Data quality and sampling rate7.9Sensor cybersecurity7.10Lab: deploying wireless sensors
8.1Preparation of condition data8.2Feature extraction8.3Anomaly detection8.4Fault classification8.5Supervised and unsupervised models8.6Deep learning for time series8.7Model validation8.8Explainability for technicians8.9Deploying models in production8.10Practical exercise: anomaly detection in Python
9.1Concept of remaining useful life9.2Physics-based models9.3Data-driven models9.4Hybrid prognostic models9.5Health indicators9.6Prognostic uncertainty9.7Maintenance decision-making9.8Integration with planning9.9Cases of major components9.10Lab: life prognosis for a bearing
10.1Equipment criticality analysis10.2Definition of routes and frequencies10.3Selection of technologies10.4Integration with the CMMS10.5Training and certification of analysts10.6Change management10.7Programme indicators10.8Programme audit10.9Continual improvement of the programme10.10Case study: predictive programme for a mining fleet
11.1Concept of the digital twin11.2Component simulation models11.3Real-time data integration11.4Fleet twins11.5Operating scenarios11.6Maintenance optimisation11.7Commercial digital twin platforms11.8Implementation challenges11.9Return on investment11.10Case study: digital twin of a transmission
12.1Selection of the asset or fleet12.2Failure mode analysis12.3Selection of techniques12.4Data architecture12.5Diagnostic model12.6Life prognosis12.7Implementation plan12.8Economic analysis of the programme12.9Master's Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
The programme is delivered 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 the intake, making it compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python with scikit-learn, MATLAB Predictive Maintenance Toolbox, Emerson AMS Machine Works and Microsoft Azure IoT, in free, academic or evaluation versions. Testing is studied through real procedures for diagnosis through vibration analysis, thermographic inspections and oil analysis programmes. Assessment is continuous by module plus the final Master's Thesis.
Standards covered: ISO 17359, ISO 13374, ISO 20816-1, ISO 18436-2, ISO 4406, ISO 18434-1, ISO 13381-1.
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
- University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- 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 1,900 hours over 12–14 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 scikit-learn, MATLAB Predictive Maintenance Toolbox, Emerson AMS Machine Works and Microsoft Azure IoT, 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 Master's Thesis. 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 in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.
A university degree in engineering, science or architecture (or equivalent professional experience), 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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