Master’s in Predictive Maintenance Engineering
Online master's programme of 1,900 hours in predictive maintenance engineering, structured in 12 modules covering maintenance strategies, vibration analysis, complementary condition monitoring techniques, sensors and data acquisition, signal processing, machine learning, remaining useful life prognosis, predictive platforms and reliability, with an assigned technical tutor.
Master’s in Predictive Maintenance Engineering — studying from Australia
Electronics and embedded systems underpin a wide range of Australian industries, including defence, mining automation, medical devices and consumer technology. The telecommunications sector is significant, with ongoing investment in fibre, mobile networks and satellite communications for remote regions. Research organisations such as CSIRO and university groups contribute to advances in sensing, communications and signal processing. Engineers typically work on hardware design, firmware development, testing and certification. Employers include telecommunications carriers, defence contractors, instrumentation companies and product manufacturers.
Fees for Australia: A$14,200 (AUD), with instalments and employer sponsorship available.
Relevant bodies in Australia
Typical roles
Questions from Australia
Is this qualification recognised for electronics engineering in Australia?
SEIUM's qualifications are private and not official state degrees, and they are not accredited by Engineers Australia or the Australian Communications and Media Authority. Recognition for electronics and telecom roles generally depends on accredited degrees, licences and experience. Employers or the relevant body make the final decision.
Which Australian employers hire embedded and telecom engineers?
Australia has telecommunications carriers, defence electronics firms, medical device makers, mining technology companies and IoT developers. Employers mostly assess your hands-on skills, project record and certifications. Ask prospective employers whether they would consider a private online qualification.
Can I study this part-time while working, and how is it supported?
The courses are 100% online and taught in English, so you can study from anywhere in Australia without needing a student visa. Specialist courses take 300 hours, diplomas 800 and master's 1,900. Each student has an assigned technical tutor, fees are payable in instalments, and employer sponsorship may be possible.
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
Design condition-based and reliability-based maintenance strategies.
Diagnose mechanical faults using vibration analysis.
Apply complementary condition monitoring techniques.
Process signals and extract features for diagnostics.
Develop machine-learning models for fault detection.
Estimate the remaining useful life of critical assets.
Implement a predictive programme with economic evaluation.
Who it's for
- Predictive maintenance engineers looking to add data-driven methods to their toolkit.
- Vibration analysts who want to move into prognosis and machine learning.
- Reliability engineers responsible for critical asset strategies.
- Industrial data scientists applying models to condition data.
- Maintenance managers building or scaling a predictive programme.
- Condition monitoring technicians seeking engineering-level expertise.
- Engineering or science graduates aiming to work in the process industry.
What you'll work with
- ISO 17359 (condition monitoring and diagnostics of machines, general guidelines)
- ISO 13374-1 (data processing, communication and presentation for condition monitoring)
- ISO 20816-1 (measurement and evaluation of machine vibration)
- ISO 18436-2 (qualification and assessment of vibration analysts)
- ISO 55001 (asset management systems, requirements)
- ISO 13379-1 (data interpretation and diagnostics techniques)
- IEC 60812 (failure modes and effects analysis, FMEA)
- EN 13306 (maintenance terminology)
- MATLAB Predictive Maintenance Toolbox
- Python with scikit-learn
- SKF @ptitude
- FLIR Thermal Studio
- Grafana
12 modules · 1900 h
1.1Corrective, preventive and predictive maintenance1.2Condition-based maintenance1.3Reliability-centred maintenance1.4P-F curve and early detection1.5Asset criticality1.6MTBF, MTTR and availability indicators1.7Life cycle costs1.8Asset management according to ISO 550001.9Maintenance maturity1.10Case study: criticality analysis of a plant
2.1Fundamentals of mechanical vibration2.2Sensors: accelerometers and proximity probes2.3Spectral analysis with FFT2.4Unbalance and misalignment2.5Looseness and resonances2.6Bearing defects and envelope analysis2.7Gear defects2.8Severity according to ISO 208162.9Phase and waveform analysis2.10Lab: bearing diagnosis with envelope spectrum
3.1Infrared thermography of equipment3.2Oil and particle analysis3.3Ultrasound and leak detection3.4Motor current signature analysis3.5Partial discharges in machines3.6Acoustic emission techniques3.7Pressure and flow monitoring3.8Automated visual inspection3.9Combining techniques3.10Practical: thermographic inspection of an electrical panel
4.1Selecting sensors for monitoring4.2MEMS versus piezoelectric sensors4.3Sampling frequency and anti-aliasing4.4Portable and online acquisition systems4.5Wireless vibration sensors4.6Sensor mounting4.7Power supply and battery life4.8Integration with PLC and SCADA4.9Quality of the acquired signal4.10Workshop: designing an online monitoring system
5.1Digital signal filtering5.2Statistical indicators in the time domain5.3Joint time-frequency analysis5.4Wavelet transform for transients5.5Spectral kurtosis and kurtogram5.6Order analysis at variable speed5.7Envelope demodulation5.8Feature extraction5.9Dimensionality reduction5.10Practical: feature extraction in Python
6.1Fault classification6.2Unsupervised anomaly detection6.3Neural networks for time series6.4Autoencoders for deviation detection6.5Random forest and boosting models6.6Class imbalance and scarce data6.7Validation of diagnostic models6.8Model interpretability6.9Transfer between machines6.10Lab: fault classification with bearing data
7.1Degradation models7.2Physics-based and data-driven approaches7.3Statistical survival models7.4Particle filters for prognosis7.5LSTM networks for useful life7.6Prognosis uncertainty7.7Prognosis evaluation metrics7.8Benchmark datasets7.9Integration with maintenance decisions7.10Practical: useful life estimation with C-MAPSS data
8.1Architecture of a predictive system8.2Edge computing for vibration8.3Cloud maintenance platforms8.4Integration with CMMS and ERP8.5Asset digital twins8.6Dashboards and alerts8.7Condition data integration standards8.8Cybersecurity of the monitoring system8.9Scaling to multiple plants8.10Case study: integrating alerts with a CMMS
9.1Electric motors and pumps9.2Compressors and fans9.3Gearboxes and transmissions9.4Turbines and generators9.5Wind turbine drivetrains9.6Power transformers9.7Machine tools and spindles9.8Bogies and railway equipment9.9Continuous process assets9.10Workshop: predictive plan for a pumping station
10.1Selection of assets and techniques10.2Defining thresholds and alarms10.3Inspection routes10.4Organisation of the reliability team10.5Analyst training and certification10.6Root cause analysis10.7Return on investment10.8Managing cultural change10.9Continuous improvement of the programme10.10Case study: return calculation for a predictive programme
11.1Failure mode and effects analysis11.2Fault trees11.3Weibull analysis11.4Reliability block diagrams11.5Maintainability and design11.6Field failure data analysis11.7Optimisation of maintenance intervals11.8Spare parts management11.9Human reliability in maintenance11.10Practical: Weibull fitting with failure data
12.1Selection of the asset and its context12.2Criticality and failure mode analysis12.3Monitoring system design12.4Data acquisition and processing12.5Development of diagnostic models12.6Prognosis and decision rules12.7Economic evaluation of the programme12.8Conclusions on the predictive programme12.9Master's Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. 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 MATLAB Predictive Maintenance Toolbox, Python with scikit-learn, SKF @ptitude, FLIR Thermal Studio and Grafana, in free, academic or evaluation versions. Testing is studied through real procedures: bearing diagnosis with vibration analysis, thermographic inspection of installations, and remaining useful life estimation with degradation data. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: ISO 17359, ISO 13374-1, ISO 20816-1, ISO 18436-2, ISO 55001, ISO 13379-1, IEC 60812, EN 13306.
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
- A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- An 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 MATLAB Predictive Maintenance Toolbox, Python with scikit-learn, SKF @ptitude, FLIR Thermal Studio and Grafana, 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 programme's 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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