CourseDefense, Military Engineering & Land Systems Applications open

Diploma in PHM Applied to Land Systems

Online postgraduate diploma of 800 hours in PHM applied to land systems: 8 modules covering PHM fundamentals under IEEE 1856-2017 and ISO 13374, on-board sensing and acquisition, health indicators, diagnostics, prognostics and remaining useful life, logistics integration and system verification, with an assigned technical tutor.

Duration6–8 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Understand the fundamentals and architecture of PHM in land systems.

02

Design on-board sensing and data acquisition strategies.

03

Extract health indicators from vehicle data.

04

Develop diagnostic and prognostic models.

05

Estimate remaining useful life with uncertainty quantification.

06

Integrate PHM into logistics support and fleet management.

07

Assess the performance and value of a PHM system.

Profile

Who it's for

  • PHM engineers.
  • Maintenance data scientists.
  • Vetronics engineers.
  • Reliability engineers.
  • Integrated logistics support managers.
  • On-board diagnostics engineers.
  • Engineering or science graduates seeking to enter the land defence sector.
Standards & tools

What you'll work with

  • IEEE 1856-2017 (prognostics and health management of electronic systems)
  • ISO 13374 (condition monitoring data processing)
  • ISO 13381-1 (prognostics)
  • SAE JA6268 (design and life-cycle management for integrated vehicle health management readiness)
  • SAE J1939 (heavy-duty vehicle networks)
  • MIL-STD-1553B (multiplexed data bus)
  • ISO 55000 (asset management)
  • MATLAB Predictive Maintenance Toolbox
  • Python (PyTorch)
  • Vector CANalyzer
  • LabVIEW
Syllabus

8 modules

  1. 1.1The concept of prognostics and health management
  2. 1.2Architecture according to ISO 13374
  3. 1.3IEEE 1856 and its framework
  4. 1.4Diagnostics versus prognostics
  5. 1.5Benefits for availability
  6. 1.6PHM in vehicle programmes
  7. 1.7Design for health readiness
  8. 1.8Challenges in land vehicles
  9. 1.9PHM maturity
  10. 1.10Case study: analysis of a PHM programme
  1. 2.1On-board sensors
  2. 2.2CAN bus and SAE J1939
  3. 2.3MIL-STD-1553B in vehicles
  4. 2.4Data loggers
  5. 2.5Sampling rate and storage
  6. 2.6Edge processing
  7. 2.7Data quality
  8. 2.8Sensor power management
  9. 2.9Environmental ruggedness
  10. 2.10Lab: CAN bus data acquisition
  1. 3.1Time-domain features
  2. 3.2Spectral features
  3. 3.3Time-frequency analysis
  4. 3.4Feature selection
  5. 3.5Normalisation by operating conditions
  6. 3.6Engine indicators
  7. 3.7Transmission indicators
  8. 3.8Battery indicators
  9. 3.9Hydraulic system indicators
  10. 3.10Practical exercise: extracting health indicators
  1. 4.1Anomaly detection
  2. 4.2Fault classification
  3. 4.3Physics-based models
  4. 4.4Data-driven models
  5. 4.5Hybrid models
  6. 4.6Diagnostic trees
  7. 4.7Diagnostics of electrical systems
  8. 4.8Validation of diagnostics
  9. 4.9Explainability
  10. 4.10Lab: fault classification with machine learning
  1. 5.1Degradation models
  2. 5.2Particle filters for prognostics
  3. 5.3Recurrent neural networks
  4. 5.4Similarity models
  5. 5.5Uncertainty quantification
  6. 5.6Prognostic metrics
  7. 5.7Battery prognostics
  8. 5.8Prognostics of mechanical components
  9. 5.9Updating with new data
  10. 5.10Practical exercise: estimating battery useful life
  1. 6.1PHM and integrated logistics support
  2. 6.2Condition-based maintenance
  3. 6.3Spare parts management
  4. 6.4Mission planning
  5. 6.5Integration with logistics information systems
  6. 6.6Fleet management
  7. 6.7Availability-based contracts
  8. 6.8Data interoperability
  9. 6.9Cybersecurity
  10. 6.10Case study: integrating PHM into maintenance planning
  1. 7.1Algorithm verification
  2. 7.2Seeded fault testing
  3. 7.3Fleet validation
  4. 7.4Cost-benefit analysis
  5. 7.5Success indicators
  6. 7.6Certification of PHM software
  7. 7.7Change management
  8. 7.8Lessons learned
  9. 7.9Future trends
  10. 7.10Workshop: value analysis of a PHM system
  1. 8.1Subsystem selection
  2. 8.2Failure mode analysis
  3. 8.3Sensing strategy
  4. 8.4Health indicators
  5. 8.5Diagnostic model
  6. 8.6Prognostic model
  7. 8.7Logistics integration
  8. 8.8System evaluation
  9. 8.9Report and presentation
  10. 8.10Final project: final review with your tutor
Methodology, software & labs

How you'll learn

Training 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 the programme can be combined with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB Predictive Maintenance Toolbox, Python (PyTorch), Vector CANalyzer and LabVIEW, in free, academic or evaluation versions. Practical work is based on real procedures: extraction of health indicators from CAN bus data, estimation of battery remaining useful life and design of an on-board PHM architecture. Assessment is continuous by module, plus the final project.

Standards covered: IEEE 1856-2017, ISO 13374, ISO 13381-1, SAE JA6268, SAE J1939, MIL-STD-1553B, ISO 55000.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of IEEE 1856-2017, ISO 13374 and ISO 13381-1 applied to real cases, not as a theoretical annex.
Industry software:MATLAB Predictive Maintenance Toolbox, Python (PyTorch), Vector CANalyzer and LabVIEW, in free, academic or evaluation versions.
Final project:a complete PHM system for a land vehicle subsystem, from failure mode analysis and sensing strategy to diagnostic and prognostic models, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Failure mode analysis
Select a vehicle subsystem and analyse its failure modes.
Sensing strategy
Define the sensing strategy and extract health indicators.
Diagnostic and prognostic models
Develop the diagnostic model and the remaining useful life model.
Logistics integration
Integrate the PHM system into logistics support and evaluate its value.
Final project
Present the complete land systems PHM project, reviewed by your tutor.
Career opportunities

Where this takes you

PHM engineerMaintenance data scientistVetronics engineerReliability engineerIntegrated logistics support managerOn-board diagnostics engineer
Admissions & scholarships

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 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.

FAQ

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 (PyTorch), Vector CANalyzer and LabVIEW, 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.

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