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Master’s in Reliability, RAMS and Warranty Analytics Engineering

Online master's programme of 1,900 hours in reliability, RAMS and warranty analytics engineering, structured in 12 modules covering RAMS concepts, applied life data statistics, warranty data and cost forecasting, early detection, reliability prediction, maintainability, reliability-centred maintenance, fleet availability, safety and risk under EN 50126-1, IEC 61703 and IEC 60300-3-1, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Apply reliability, availability, maintainability and safety concepts to two-wheeled vehicles.

02

Model component life using censored field data.

03

Estimate and forecast warranty costs with statistical models.

04

Design reliability-centred maintenance strategies.

05

Build early detection systems for field problems.

06

Integrate reliability into the product development cycle.

07

Develop a warranty analytics system with RAMS models.

Profile

Who it's for

  • Product reliability engineers in vehicle or component manufacturing.
  • RAMS engineers looking to apply their methods to two-wheelers and fleets.
  • Warranty analytics analysts working with claims data.
  • Quality data scientists who model field failures.
  • Maintainability engineers focused on repair times and access.
  • Field reliability managers responsible for in-service performance.
  • Engineering or science graduates aiming to join motorcycle and scooter manufacturers.
Standards & tools

What you'll work with

  • EN 50126-1 (RAMS specification and demonstration — generic RAMS process)
  • IEC 61703 (mathematical expressions for reliability, availability, maintainability and maintenance support terms)
  • IEC 60300-3-1 (dependability management — analysis techniques)
  • IEC 61709 (electric components — reliability reference conditions for failure rates)
  • IEC 62308 (equipment reliability — reliability assessment methods)
  • ISO 26262-12 (functional safety — adaptation for motorcycles)
  • IEC 61164 (reliability growth — statistical test and estimation methods)
  • SAE JA1011 (evaluation criteria for reliability-centred maintenance processes)
  • ReliaSoft Weibull++
  • ReliaSoft RGA
  • Python with lifelines
  • R
  • Power BI
Syllabus

12 modules · 1900 h

  1. 1.1Reliability, availability, maintainability and safety
  2. 1.2RAMS life cycle according to EN 50126
  3. 1.3Product RAMS requirements
  4. 1.4MTBF, MTTR and availability metrics
  5. 1.5Bathtub curve
  6. 1.6Design, manufacturing and usage failures
  7. 1.7RAMS in fleets and for private customers
  8. 1.8Life cycle cost
  9. 1.9RAMS management in the company
  10. 1.10Case study: RAMS requirements for a fleet scooter
  1. 2.1Life distributions
  2. 2.2Right-censored data
  3. 2.3Truncation at end of warranty
  4. 2.4Kaplan-Meier estimator
  5. 2.5Weibull with field data
  6. 2.6Cox proportional hazards models
  7. 2.7Usage versus calendar time
  8. 2.8Combined mileage and age
  9. 2.9Model selection
  10. 2.10Lab: survival analysis with lifelines
  1. 3.1Structure of warranty databases
  2. 3.2Data quality and cleansing
  3. 3.3Reporting lag
  4. 3.4Handling missing mileage
  5. 3.5Fault codes
  6. 3.6Linking with production data
  7. 3.7Sales and registration data
  8. 3.8Telemetry data
  9. 3.9Data governance
  10. 3.10Practical exercise: cleansing a warranty database
  1. 4.1Months-in-service matrices
  2. 4.2Claim rates
  3. 4.3Forecasting with life models
  4. 4.4Warranty accounting provisions
  5. 4.5Forecast uncertainty
  6. 4.6Campaign scenarios
  7. 4.7Extended warranty contracts
  8. 4.8Communication to finance
  9. 4.9Deviation tracking
  10. 4.10Workshop: warranty forecasting model
  1. 5.1Early failure indicators
  2. 5.2Control charts for warranty
  3. 5.3Change detection
  4. 5.4Analysis by production batch
  5. 5.5Analysis by supplier
  6. 5.6Text mining of claims
  7. 5.7Automated quality alerts
  8. 5.8Problem prioritisation
  9. 5.9Closing the loop with engineering
  10. 5.10Lab: early warnings with Python
  1. 6.1Failure rate databases
  2. 6.2Prediction according to IEC 61709
  3. 6.3Block diagrams
  4. 6.4Fault trees
  5. 6.5Markov analysis
  6. 6.6Reliability allocation
  7. 6.7Reliability demonstration to IEC 61164
  8. 6.8Reliability growth
  9. 6.9Software reliability
  10. 6.10Practical exercise: reliability prediction for an electronic instrument cluster
  1. 7.1Design for maintenance
  2. 7.2Repair times
  3. 7.3Component accessibility
  4. 7.4Diagnosis and fault location
  5. 7.5Maintainability in fleets
  6. 7.6Special workshop tools
  7. 7.7Battery maintainability
  8. 7.8Maintainability demonstration
  9. 7.9Process monitoring indicators
  10. 7.10Case study: improving the maintainability of a scooter
  1. 8.1RCM maintenance methodology
  2. 8.2SAE JA1011 criteria
  3. 8.3Functions and functional failures
  4. 8.4Failure consequences
  5. 8.5Preventive and predictive tasks
  6. 8.6Optimal maintenance intervals
  7. 8.7Condition-based maintenance
  8. 8.8Fleet maintenance
  9. 8.9Cost evaluation
  10. 8.10Workshop: RCM analysis of a motorcycle fleet
  1. 9.1Availability models
  2. 9.2Fleet simulation
  3. 9.3Spare parts and availability
  4. 9.4Logistic repair times
  5. 9.5In-service failures
  6. 9.6Fleet indicators
  7. 9.7Availability improvement
  8. 9.8Availability contracts
  9. 9.9Fleet telemetry
  10. 9.10Lab: availability simulation of a fleet
  1. 10.1Failures with safety impact
  2. 10.2Functional safety to ISO 26262-12
  3. 10.3Field risk analysis
  4. 10.4Recall criteria
  5. 10.5Incident investigation
  6. 10.6Evidence management
  7. 10.7Communication with authorities
  8. 10.8Product liability
  9. 10.9Lessons learned log
  10. 10.10Case study: decision on a safety campaign
  1. 11.1Programme reliability plan
  2. 11.2Targets by system
  3. 11.3Validation tests
  4. 11.4Accelerated life tests
  5. 11.5Test-to-field correlation
  6. 11.6Design reviews
  7. 11.7Supplier reliability
  8. 11.8Technical risk management
  9. 11.9Maturity indicators
  10. 11.10Practical exercise: reliability plan for a new model
  1. 12.1Product definition and data
  2. 12.2Data cleansing
  3. 12.3Life modelling
  4. 12.4Cost forecasting
  5. 12.5Early detection system
  6. 12.6Maintainability analysis
  7. 12.7Improvement proposals
  8. 12.8Dashboard
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
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 timetables: you progress at your own pace within each intake, making it compatible with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is ReliaSoft Weibull++, ReliaSoft RGA, Python with lifelines, R and Power BI, in free, academic or evaluation versions. Practical work is based on real procedures: estimation of the B10 life of a component from warranty data, an availability model for a rental scooter fleet and warranty cost forecasting for a new model. Assessment is continuous by module plus the final Master's Thesis.

Standards covered: EN 50126-1, IEC 61703, IEC 60300-3-1, IEC 61709, IEC 62308, ISO 26262-12, IEC 61164, SAE JA1011.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of EN 50126-1, IEC 61703, IEC 60300-3-1 and IEC 61709 applied to real cases, not as a theoretical annex.
Industry software:ReliaSoft Weibull++, ReliaSoft RGA, Python with lifelines, R and Power BI, in free, academic or evaluation versions.
Master's Thesis with defence:a warranty analytics system with RAMS models, from data cleansing and life modelling to cost forecasting, early detection, maintainability analysis and a dashboard, defended before a panel.
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.

Warranty data cleansing
Cleanse and structure a warranty database, handling reporting lag and missing mileage.
Survival analysis
Model component life with Kaplan-Meier and Weibull on censored field data.
Warranty cost forecast
Build a warranty forecasting model with months-in-service matrices and uncertainty.
Early warning alerts
Create automated early warning alerts with Python and fleet availability simulation.
Master's Thesis
A warranty analytics system with RAMS models, from data cleansing and life modelling to cost forecasting, early detection, maintainability analysis and a dashboard, defended before a panel.
Career opportunities

Where this takes you

Product reliability engineerRAMS engineerWarranty analytics analystQuality data scientistMaintainability engineerField reliability manager
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • 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.

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FAQ

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 ReliaSoft Weibull++, ReliaSoft RGA, Python with lifelines, R 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 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, plus 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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