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CourseDefense, Military Engineering & Land Systems Applications open

Standards in Military Predictive Maintenance Course

Online 300 h course in standards in military predictive maintenance. 6 modules covering ISO 17359 (condition monitoring), ISO 13374 (processing of monitoring data) and MATLAB (Predictive Maintenance Toolbox). Assigned tutor and open enrolment.

Duration300 h
ModeOnline
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Learning outcomes

What you will be able to do

01

Distinguish between corrective, preventive and predictive maintenance strategies.

02

Apply vibration analysis, thermography and oil analysis diagnostic techniques.

03

Design a sensorisation and data acquisition architecture.

04

Build remaining useful life prediction models.

05

Apply the RCM methodology in accordance with SAE JA1011.

06

Plan the implementation and management of a predictive programme.

07

Develop a predictive maintenance project for a real system.

Profile

Who it's for

  • Predictive maintenance engineer who wants to specialise in Standards in military predictive maintenance.
  • Reliability analyst who wants to specialise in Standards in military predictive maintenance.
  • Industrial data scientist who wants to specialise in Standards in military predictive maintenance.
  • Asset management manager who wants to specialise in Standards in military predictive maintenance.
  • Vibration analysis technician who wants to specialise in Standards in military predictive maintenance.
  • Integrated logistics support engineer who wants to specialise in Standards in military predictive maintenance.
  • Engineering or science graduates seeking to enter the defence industry.
Standards & tools

What you'll work with

  • ISO 17359 (condition monitoring)
  • ISO 13374 (processing of monitoring data)
  • ISO 13381-1 (prognostics)
  • SAE JA1011 (RCM criteria)
  • IEEE 1856-2017 (PHM of electronic systems)
  • ISO 20816 (mechanical vibration)
  • ISO 55001 (asset management)
  • MIL-STD-3034A (reliability-centred maintenance)
Syllabus

6 modules · 300 h

  1. 1.1Maintenance strategies and their cost
  2. 1.2Failure curve and component life
  3. 1.3Condition-based maintenance
  4. 1.4Criteria for choosing the strategy per asset
  5. 1.5Asset criticality and risk analysis
  6. 1.6Maintenance and availability indicators
  7. 1.7Reliability-centred maintenance
  8. 1.8Reference standards for asset management
  9. 1.9Field vocabulary
  10. 1.10Exercise: classifying assets by criticality
  1. 2.1Vibration analysis and its interpretation
  2. 2.2Spectra, envelopes and bearing defects
  3. 2.3Infrared thermography
  4. 2.4Oil analysis and wear particle analysis
  5. 2.5Ultrasonics applied to leaks and bearings
  6. 2.6Current and electrical signature analysis
  7. 2.7Non-destructive testing in maintenance
  8. 2.8Assisted visual inspection and endoscopy
  9. 2.9Selecting the technique according to failure mode
  10. 2.10Practice: diagnosing a case with real data
  1. 3.1Sensor selection and placement
  2. 3.2Sampling, resolution and synchronisation
  3. 3.3Sensor networks and communications
  4. 3.4Power supply and field autonomy
  5. 3.5Robustness in harsh environments
  6. 3.6Data storage and architecture
  7. 3.7Data quality and event labelling
  8. 3.8Integration with the maintenance management system
  9. 3.9Cybersecurity of the sensor network
  10. 3.10Practice: designing the sensorisation of an equipment item
  1. 4.1Thresholds, alarms and their tuning
  2. 4.2Anomaly detection without failure data
  3. 4.3Data-driven models and machine learning
  4. 4.4Physical and degradation models
  5. 4.5Remaining life estimation
  6. 4.6Model validation and metrics
  7. 4.7False positives and the cost of false alarms
  8. 4.8Explainability of alerts for technicians
  9. 4.9Model updating and drift
  10. 4.10Practice: building a detection model
  1. 5.1Business case for predictive maintenance
  2. 5.2Pilot project and scaling up
  3. 5.3Integration into the technician's workflow
  4. 5.4Dynamic scheduling of interventions
  5. 5.5Spare parts and logistics management
  6. 5.6Training and team acceptance
  7. 5.7Success indicators for the programme
  8. 5.8Availability-based maintenance contracts
  9. 5.9Document management and traceability
  10. 5.10Practice: implementation plan for a pilot
  1. 6.1Choice of fleet or plant
  2. 6.2Criticality and failure mode analysis
  3. 6.3Selection of diagnostic techniques
  4. 6.4Design of sensorisation and data
  5. 6.5Development of the predictive model
  6. 6.6Integration with maintenance management
  7. 6.7Business case and economic analysis
  8. 6.8Implementation and training plan
  9. 6.9Technical report
  10. 6.10Project defence
Methodology, software & labs

How you'll learn

100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetable: students progress at their own pace within the enrolment period, which makes it easier to fit around work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB (Predictive Maintenance Toolbox), Python (scikit-learn), IBM Maximo, SAP PM, in free, academic or evaluation versions. Tests are studied using real bearing test-rig procedures, analysis of real vibration data, and an RCM study of a subsystem. Assessment is continuous across modules plus the final integrating project.

Accreditable laboratories(EN ISO/IEC 17025) with a rotor rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-driven Master's Thesis:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:lecturers with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:a safety-by-design approach, with OT cybersecurity, DIH and compliance as its pillars.
Capstone project

Build it end to end

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

Choice of fleet or plant

Distinguish between corrective, preventive and predictive maintenance strategies.

Criticality and failure mode analysis

Apply vibration analysis, thermography and oil analysis diagnostic techniques.

Selection of diagnostic techniques

Design a sensorisation and data acquisition architecture.

Design of sensorisation and data

Build remaining useful life prediction models.

Development of the predictive model

Apply the RCM methodology in accordance with SAE JA1011.

Career opportunities

Where this takes you

Predictive maintenance engineerReliability analystIndustrial data scientistAsset management managerVibration analysis technicianIntegrated logistics support engineer
Admissions & scholarships

Requirements

  • No prior qualification is required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient command of Spanish to follow technical standards documentation.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme consists of 300 hours in total, spread over 3 to 5 months. That is between 10 and 15 hours a week on average, compatible with a full-time job.

You will work with MATLAB (Predictive Maintenance Toolbox), Python (scikit-learn), IBM Maximo, SAP PM. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

SEIUM's own diploma upon passing the continuous assessment and the integrating project. This is in-house training, not an official qualification from the Ministry.

Yes. Modules passed in a course or postgraduate diploma in the same area count towards the higher-level programme. Check the recognition policy when you enrol.

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