Master’s in PHM/Predictive Maintenance of Military Fleets Engineering
Online master's programme of 1,900 hours in PHM / predictive maintenance engineering for military fleets. 12 modules covering asset health management, sensors and data acquisition, signal processing, fault diagnosis, remaining useful life prognosis, fleet digital twins, data platforms, maintenance decision-making and certification under ISO 13374, ISO 17359 and SAE ARP6461, with an assigned technical tutor.
Master’s in PHM/Predictive Maintenance of Military Fleets Engineering — studying from Australia
Defence is one of Australia's largest engineering employers, with major programmes in land systems, naval platforms, aircraft and integrated systems managed through the Department of Defence. Land systems engineering supports armoured vehicles, weapons and logistics equipment, often through prime contractors and local industry partners. Industry involvement is strongly encouraged through the Defence industry policy framework. Engineers work on sustainment, capability development, systems integration and safety assurance, typically in Canberra, Adelaide, Melbourne and regional defence precincts.
Fees for Australia: A$15,800 (AUD), with instalments and employer sponsorship available.
Relevant bodies in Australia
Typical roles
Questions from Australia
How does defence engineering recognition work in Australia?
Defence engineering in Australia is largely run through the Department of Defence and its industry partners, which set their own security clearance and qualification requirements. Engineers Australia is the professional body for accreditation. SEIUM's private qualifications are not accredited by these bodies, so recognition is for employers or the relevant body to decide.
Which Australian employers hire in defence and land systems?
Australia has defence prime contractors, the Department of Defence, and specialist land systems firms. Many of these roles require Australian security clearances, which depend on citizenship and background checks rather than course completion. Ask employers how they weigh private qualifications against experience and clearance requirements.
Is the course manageable with a job, and does it need a visa?
The programmes are 100% online and taught in English, so you can study from Australia without a student visa. Specialist courses take 300 hours, diplomas 800 and master's 1,900. An assigned technical tutor supports you, 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
Understand the architecture of asset health management systems.
Acquire and process signals from onboard sensors.
Diagnose faults using physics-based and data-driven models.
Estimate remaining useful life with uncertainty quantification.
Develop digital twins of components and fleets.
Integrate prognostics into maintenance decision-making.
Apply condition monitoring and HUMS standards and regulations.
Who it's for
- PHM engineers
- Maintenance data scientists
- Reliability engineers
- Digital twin engineers
- Fleet maintenance managers
- Condition monitoring engineers
- Engineering or science graduates looking to enter the defence sector
What you'll work with
- ISO 13374 (condition monitoring and diagnostics of machines – data processing)
- ISO 17359 (condition monitoring and diagnostics – general guidelines)
- SAE ARP6461 (guidelines for HUMS in civil helicopters)
- SAE JA1011 (evaluation criteria for RCM processes)
- ISO 55001 (asset management systems)
- SAE J1939 (vehicle network communications)
- IEEE 1856 (prognostics and health management of electronic systems)
- MATLAB Predictive Maintenance Toolbox
- Python
- TensorFlow
- InfluxDB
- Grafana
12 modules · 1900 h
1.1PHM concept: diagnostics and prognostics1.2Evolution of maintenance towards predictive maintenance1.3Fleet availability and cost1.4OSA-CBM architecture1.5ISO 13374 standard1.6Success stories in aviation and vehicles1.7Economic benefits1.8Organisational challenges1.9Ethics and data use1.10Case study: PHM business case for a truck fleet
2.1Vibration sensors and accelerometers2.2Temperature and pressure sensors2.3Online oil analysis2.4Current and voltage sensors2.5CAN and J1939 vehicle buses2.6HUMS systems in helicopters2.7Acquisition and sampling2.8Onboard edge computing2.9Data quality2.10Lab: CAN data acquisition with Python
3.1Time-domain analysis3.2Fourier transform and spectrum3.3Envelope analysis for bearings3.4Cepstrum and gears3.5Time-frequency analysis and wavelets3.6Feature extraction3.7Synchronous averaging3.8Normalisation by operating conditions3.9Feature selection3.10Practical exercise: bearing diagnostics with CWRU data
4.1Failure modes of mechanical components4.2Model-based diagnostics4.3Data-driven diagnostics4.4Supervised classifiers4.5Unsupervised anomaly detection4.6Diesel engine diagnostics4.7Transmission diagnostics4.8Electrical system diagnostics4.9Evaluation of diagnostics4.10Lab: anomaly detection with autoencoders
5.1Definition of remaining useful life (RUL)5.2Physical degradation models5.3Statistical and Weibull models5.4Particle filters for prognosis5.5LSTM networks and transformers5.6Uncertainty quantification5.7Prognostic metrics5.8C-MAPSS and PHM Society datasets5.9Deployment of prognostics5.10Practical exercise: RUL estimation with LSTM on C-MAPSS
6.1Digital twin concept6.2Physical and reduced-order models6.3Data assimilation6.4Component and fleet twins6.5Industrial platforms6.6Integration with PLM6.7Simulation of usage scenarios6.8Twin validation6.9Scaling to fleets6.10Workshop: simplified digital twin of a transmission
7.1Fleet data architectures7.2Ingestion and storage7.3Data lakes and time-series databases7.4Distributed processing7.5MLOps for PHM7.6Health dashboards7.7Integration with maintenance systems7.8Fleet data cybersecurity7.9Data sovereignty7.10Lab: pipeline with InfluxDB and Grafana
8.1From prognosis to work order8.2Optimisation of maintenance windows8.3Prognosis-based spare parts management8.4Opportunistic maintenance8.5Cost-risk models8.6Fleet availability planning8.7Integration with RCM8.8Effectiveness indicators8.9Change management8.10Case study: maintenance optimisation for a fleet
9.1Wheeled land vehicles9.2Tracked vehicles9.3Helicopters and HUMS9.4Fixed-wing aircraft9.5Ships and naval propulsion9.6Unmanned systems9.7Generator sets9.8Weapons: a reliability approach9.9Comparison of solutions9.10Practical exercise: HUMS analysis with public data
10.1ISO 13374 and 17359 standards10.2SAE ARP6461 for HUMS10.3Maintenance credits in aviation10.4Algorithm accreditation10.5Critical software and certification10.6Data regulation10.7Quality standards10.8PHM audits10.9Configuration management10.10Workshop: certification requirements for a HUMS
11.1Implementation roadmap11.2Pilots and scaling11.3Technician training11.4Data culture11.5Suppliers and contracts11.6Return on investment11.7PHM project management11.8Lessons learned11.9Technology trends11.10Case study: PHM implementation plan
12.1Definition of the fleet and component12.2Failure mode analysis12.3Data acquisition and preparation12.4Diagnostics and features12.5Prognostic model12.6Integration into maintenance decision-making12.7Economic evaluation12.8Conclusions and future work12.9Master'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. There are no fixed timetables: you progress at your own pace within the intake, so you can combine it with your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB Predictive Maintenance Toolbox, Python, TensorFlow, InfluxDB and Grafana, in free, academic or evaluation versions. Real procedures are studied, such as bearing diagnostics with vibration signals, remaining useful life estimation with deep learning and fleet health dashboards. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: ISO 13374, ISO 17359, SAE ARP6461, SAE JA1011, ISO 55001, SAE J1939, IEEE 1856.
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, TensorFlow, InfluxDB 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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