Diploma in Fault Diagnosis and Prognosis in Defence
Online postgraduate diploma of 800 hours in fault diagnosis and prognosis in defence, structured in 8 modules covering reliability and failure modes, sensors and data acquisition, signal processing, diagnostic methods, prognosis and remaining useful life, integration into sustainment, and certification under ISO 13374, MIL-STD-1629A and ISO 17359, with an assigned technical tutor.
Diploma in Fault Diagnosis and Prognosis in Defence — studying from Singapore
Singapore maintains a comprehensive defence sector through the Ministry of Defence and the Defence Science and Technology Agency, which develops military technologies and manages procurement. Engineering work covers land systems, ammunition, command and control, and simulation. Industry partners include ST Engineering, which supplies defence and security systems globally. Engineers here work within strict security and export control frameworks, often combining systems integration with reliability and safety engineering.
Fees for Singapore: S$1,840 (Singapore dollar), with instalments and employer sponsorship available.
Relevant bodies in Singapore
Typical roles
Questions from Singapore
How does defence engineering work in Singapore, and which bodies oversee it?
Singapore's defence engineering is largely centred on the Ministry of Defence (MINDEF) and DSTA, the defence procurement and development agency, along with the Singapore Armed Forces. Engineers may register with the Professional Engineers Board (PEB). SEIUM's qualifications are private, not state degrees, and not accredited by any national body, so recognition for defence roles depends on the employer or relevant authority.
Who employs defence and land systems engineers in Singapore?
Main employers include MINDEF, DSTA, the SAF, and defence industry companies including ST Engineering. Access to many roles requires security clearance and citizenship or residency rules, so check eligibility early. Employers focus on proven competence and clearance status. Your final project can illustrate technical capability, but it does not replace these requirements.
Is the course manageable alongside a job, and can employers fund it?
The programme is fully online and taught in English, with an assigned technical tutor. Plan for a heavy workload: 1,900 hours for a master's, 800 for a postgraduate diploma, or 300 for a specialist course. Fees are payable in instalments, and employer sponsorship is possible, which is common in defence-linked organisations, though approval is up to them.
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
Analyse the failure modes and reliability of complex systems.
Select sensors and data acquisition strategies.
Apply signal processing techniques for diagnosis.
Develop data-driven and model-based diagnostic models.
Estimate remaining useful life with uncertainty quantification.
Integrate diagnosis into sustainment strategies.
Validate monitoring systems in line with regulations.
Who it's for
- Reliability engineers working on defence platforms.
- Predictive maintenance specialists.
- Industrial data scientists applying machine learning to equipment health.
- Integrated logistic support engineers.
- HUMS systems analysts.
- Fleet sustainment managers.
- Engineering or science graduates looking to enter the defence sector.
What you'll work with
- ISO 13374 (condition monitoring and diagnostics of machines)
- MIL-STD-1629A (FMECA analysis)
- ISO 17359 (condition monitoring general guidelines)
- SAE ARP5783 (Health and Usage Monitoring Systems)
- MIL-HDBK-217F (reliability prediction)
- ISO 10816 (evaluation of machine vibration)
- SAE JA1011 (reliability-centred maintenance)
- MATLAB Predictive Maintenance Toolbox
- Python scikit-learn
- ReliaSoft Weibull++
- LabVIEW
- TensorFlow
8 modules
1.1Reliability and availability concepts1.2Life distributions: Weibull and exponential1.3Bathtub curve1.4FMECA analysis according to MIL-STD-1629A1.5Fault trees1.6Physical degradation mechanisms1.7Fatigue, corrosion and wear1.8Electronic and thermal failures1.9Failure databases1.10Case study: FMECA of a hydraulic subsystem
2.1Accelerometers and vibration analysis2.2Infrared thermography2.3Oil and particle analysis2.4Acoustic emission2.5Strain gauges and strain measurement2.6Current and voltage sensors2.7HUMS systems in helicopters2.8Acquisition and sampling rate2.9Data quality and cleansing2.10Lab: vibration acquisition on a bearing test rig
3.1Spectral analysis with FFT3.2Envelope analysis3.3Time-frequency analysis and wavelets3.4Condition indicators3.5Kurtosis and crest factors3.6Order analysis in rotating machinery3.7Filtering and noise reduction3.8Feature extraction3.9Indicator selection3.10Practical exercise: diagnosis of gear defects
4.1Model-based diagnosis4.2Observers and residuals4.3Expert systems and rules4.4Classification with machine learning4.5Convolutional neural networks4.6Diagnosis of electrical systems4.7Diagnosis in turbine engines4.8Fault isolation4.9Classifier validation4.10Lab: fault classifier with scikit-learn
5.1Concept of remaining useful life5.2Physics-based models5.3Data-driven models5.4Particle filters5.5LSTM networks for time series5.6Uncertainty quantification5.7C-MAPSS datasets5.8Prognostic metrics5.9Updating models in service5.10Practical exercise: remaining useful life estimation with turbofan data
6.1Condition-based maintenance6.2OSA-CBM architecture6.3ISO 13374 standard6.4Integration with logistics systems6.5Spare parts management6.6Life cycle cost6.7Integrated logistic support6.8Cybersecurity of maintenance data6.9Change management in workshops6.10Case study: fleet sustainment plan
7.1Validation of diagnostic algorithms7.2Airworthiness requirements7.3Maintenance credits7.4SAE standards for HUMS7.5Software verification7.6False alarm management7.7Technical documentation7.8Data governance7.9Ethical aspects of automation7.10Case study: validation of a HUMS system
8.1Selection of the study system8.2Failure mode analysis8.3Sensor definition8.4Processing and features8.5Diagnostic model8.6Prognostic model8.7Integration into sustainment8.8Performance evaluation8.9Report and presentation8.10Final project: final review with the tutor
How you'll learn
The programme 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 it fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. You work with MATLAB Predictive Maintenance Toolbox, Python scikit-learn, ReliaSoft Weibull++, LabVIEW and TensorFlow, in free, academic or evaluation versions. Testing is studied through real procedures for vibration acquisition on bearing test rigs, analysis of turbine degradation data and validation of fault classifiers. Assessment is continuous by module, plus the final project.
Standards covered: ISO 13374, MIL-STD-1629A, ISO 17359, SAE ARP5783, MIL-HDBK-217F, ISO 10816 and SAE JA1011.
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 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 MATLAB Predictive Maintenance Toolbox, Python scikit-learn, ReliaSoft Weibull++, LabVIEW and TensorFlow, 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.
Related programmes
Diploma in Directed Energy Systems Validation
Online postgraduate diploma of 800 hours in directed energy systems validation. 8 modules covering directed energy fundamentals, verification and validation engineering, beam and high-power RF metrology,…
Diploma in Military GNSS and Critical Standards
Online postgraduate diploma of 800 hours in military GNSS and critical standards. 8 modules covering constellations and signals, governmental services, receiver architecture, interference, PNT resilience, military…
Diploma in Military AM Standards and Certification
Online postgraduate diploma of 800 hours in military additive manufacturing (AM) standards and certification. 8 modules covering the AM regulatory landscape, feedstock and process qualification, part…
More in Defense, Military Engineering & Land Systems
All Defense, Military Engineering & Land Systems programmes → · How to apply · Fees & financing · Scholarships