Diploma in PHM and Military Predictive Maintenance
600-hour online postgraduate diploma in PHM and military predictive maintenance: sensors, signal processing, diagnostics, remaining useful life, logistics integration, human factors and data governance.
Diploma in PHM and Military Predictive Maintenance — studying from South Africa
South Africa has a established defence industry centred on companies such as Denel and Paramount Group, with capabilities in land systems, armoured vehicles and munitions. The sector supports engineering roles in systems integration, vehicle design and test and evaluation. Defence procurement and export controls are overseen by government bodies, and the industry works closely with the South African National Defence Force. Engineers in this field typically combine mechanical, electrical and systems skills.
Fees for South Africa: R 16 700 (South African rand), with instalments and employer sponsorship available.
Relevant bodies in South Africa
Typical roles
Questions from South Africa
Which South African bodies regulate or recognise defence and military engineering professionals?
Engineers in South Africa are registered through ECSA under the Engineering Profession Act, and defence programmes are guided by institutions such as the South African National Defence Force and Armscor, which set their own requirements. SEIUM is a private online institute, and its qualifications are not state degrees or accredited by a national body. Recognition is decided by these bodies and your employer, so confirm with them first.
Would this qualification help me find work in South Africa's defence or land systems sector?
South Africa has a defence industry with state-linked entities and private contractors, and employers usually require security clearances, relevant experience and recognised qualifications. A private online programme may support your application but does not guarantee employment or clearance. Check requirements with prospective employers, as roles in this sector can be sensitive and tightly controlled.
Can I study alongside a job, and are there funding options?
The course is 100% online and taught in English, so you need strong English, and no student visa is needed to study from home. Master's programmes are 1,900 hours, postgraduate diplomas 800 hours and specialist courses 300 hours, each with an assigned technical tutor. Fees are payable in instalments, and employer sponsorship is possible, so ask whether your employer would fund part of the 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 candidate failure modes for monitoring using FMEA and criticality criteria.
Specify sensors, data acquisition and on-board computing for a platform in a harsh environment.
Extract condition indicators for gears and bearings through signal processing.
Build diagnostic and prognostic models with uncertainty quantification.
Integrate alerts into work orders, spares inventory and maintenance planning.
Design the maintainer’s interface and procedures taking human factors into account.
Govern programme data: ownership, cybersecurity, airworthiness and export control.
Who it's for
- Maintenance and reliability engineers for vehicle, ship and aircraft fleets.
- Data analysts working with vibration, temperature or oil signals.
- Logistic support managers who want to reduce unscheduled downtime.
- Embedded systems engineers integrating sensors and edge computing.
- Oil analysis and machinery diagnostics laboratory technicians.
- Industrial, aeronautical or telecommunications engineering graduates interested in condition-based maintenance.
What you'll work with
- ISO 13374 (condition monitoring data processing and presentation)
- ISO 17359 (general guidelines for condition monitoring)
- ISO 13381-1 (prognostics in condition monitoring)
- ISO 20816-1 (measurement and evaluation of machine vibration)
- IEEE 1856 (framework for prognostics and health management of electronic systems)
- SAE JA1011 (criteria for reliability-centred maintenance)
- MIL-STD-1629A (failure mode, effects and criticality analysis)
- CAP 753 (helicopter vibration health monitoring)
8 modules
1.1Corrective, preventive and condition-based maintenance1.2Maturity levels of predictive capability1.3Diagnostics, prognostics and remaining useful life1.4OSA-CBM layered functional architecture1.5Failure modes that are candidates for monitoring1.6FMEA oriented towards monitoring1.7Criticality and selection of the instrumented chain1.8Business case for predictive capability1.9Prognostic performance metrics1.10Standards and guidance applicable to the programme
2.1Accelerometers, thermocouples and strain gauges2.2Oil debris sensors2.3Acoustic emission and passive ultrasound2.4Sampling frequency and anti-aliasing filter2.5Time synchronisation and phase reference2.6Sensor installation and mounting noise2.7Power, cabling and wireless energy harvesting2.8On-board and edge computing capacity2.9Environmental qualification of the installed sensor2.10Bandwidth and data download
3.1Spectral analysis and the fast Fourier transform3.2Shaft-angle synchronous averaging3.3Signal envelope and demodulation3.4Cepstrum and detection of harmonic families3.5Gear condition indicators3.6Bearing condition indicators3.7Kurtosis, crest factor and skewness3.8Wavelet transform for transient signals3.9Removing the influence of operating regime3.10Feature selection and dimensionality reduction
4.1Physics-of-degradation models4.2Data-driven models and supervised learning4.3Hybrid methods and the digital twin4.4Adaptive thresholds and novelty detection4.5Remaining useful life estimated with a particle filter4.6Quantifying prognostic uncertainty4.7Accuracy, precision and horizon metrics4.8Scarce failure data and data augmentation4.9Accelerated degradation tests for training4.10Model verification and validation
5.1Intervention decision based on the alert5.2Rescheduling downtime and the window of opportunity5.3Impact on spares inventory5.4Replenishment driven by predicted condition5.5Integration with the maintenance management system5.6Flow of the generated work order5.7Maintenance levels and removal decision5.8Configuration management of the monitored asset5.9Component traceability and history5.10Availability and avoided cost indicators
6.1Operating in austere environments without connectivity6.2Maintainer interface and alert presentation6.3Operator trust in the system6.4Managing false alarms and missed detections6.5Training personnel to interpret data6.6Procedure for alerts without physical evidence6.7Authority to postpone an intervention6.8Documenting the decision taken6.9Feeding the outcome back into the model6.10Programme governance and its owners
7.1Data ownership between manufacturer and user7.2Data use agreements in support contracts7.3Information classification and custody7.4Cybersecurity of the monitoring system7.5Impact on airworthiness and its approval7.6Evidence to extend inspection intervals7.7Change management of the deployed model7.8Auditing system performance7.9Export control of algorithms and data7.10Ethics of using personnel performance data
8.1Selecting the subsystem and analysing its failures8.2Defining the failure modes to monitor8.3Specifying sensors and their location8.4Data acquisition and management plan8.5Calculating the chosen condition indicators8.6Training the model with the available data8.7Defining thresholds and alert logic8.8Integration with work orders and spares8.9Economic case with savings and availability8.10Phased deployment plan and its defence
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 you can combine the programme with your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB and Predictive Maintenance Toolbox, Python (NumPy, SciPy, scikit-learn), LabVIEW, Power BI, in free, academic or evaluation versions. Cases are studied on real vibration datasets, oil analysis results and accelerated degradation tests of transmissions, engines and bearings. Assessment is continuous, module by module, plus the final integrated project.
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.
- An internet connection and a computer able to run the programme 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 600 hours over 6–9 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with MATLAB and Predictive Maintenance Toolbox, Python (NumPy, SciPy, scikit-learn), LabVIEW and Power BI, 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 programme 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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