Master’s in Predictive Maintenance with AI
Online master's programme of 1,900 hours in predictive maintenance with AI: vibration, acoustics, asset health and partial digital twins. 12 modules covering maintenance strategy, reliability, condition monitoring, anomaly detection, fault diagnosis, remaining useful life and deployment, with ISO 13374, ISO 17359 and ISO 20816, and an assigned technical tutor.
Master’s in Predictive Maintenance with AI — studying from the United Kingdom
Data science, artificial intelligence and simulation are central to engineering practice across the United Kingdom, with strong hubs in London, Cambridge, Oxford, Bristol and Edinburgh. Universities, research institutes such as the Alan Turing Institute and industrial users in aerospace, automotive, energy and finance demand skills in modelling, machine learning and computational analysis. Engineering simulation tools are widely used for design validation and digital twins. Organisations seek professionals who can turn large datasets into engineering decisions while meeting data protection and governance requirements.
Fees for the United Kingdom: £5,770 (Pound sterling), with instalments and employer sponsorship available.
Relevant bodies in the United Kingdom
Typical roles
Questions from the United Kingdom
Which UK bodies recognise data and AI qualifications?
In the UK, the British Computer Society (BCS), the Royal Statistical Society (RSS) and the Institute of Analytics are relevant professional bodies, and the IET covers many engineering-related AI and data areas. SEIUM's qualifications are private and not accredited by these bodies. Check directly with BCS or RSS whether your study would be considered towards their membership or certification.
Do UK employers in data and AI value private online qualifications?
UK employers in finance, retail, health, technology and public sector analytics typically assess candidates on technical skills, portfolio work and experience with real data. A private qualification may help demonstrate specialist learning, but its value depends on each employer's judgement. Publishing projects and showing practical results will usually matter most.
Can I study this online from the UK alongside work, and what English level is required?
Yes, the programmes are fully online and can be studied from anywhere in the UK without a student visa. Teaching is in English, so you need a solid working level of technical English. An assigned technical tutor will support you. Check the programme hours against your working commitments and ask about instalment payments or employer sponsorship.
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 maintenance strategies based on criticality and failure modes.
Apply condition monitoring techniques to machinery.
Prepare industrial data for predictive models.
Develop anomaly detection and fault diagnosis models.
Estimate remaining useful life with uncertainty quantification.
Integrate predictions into maintenance planning.
Evaluate the economic return of predictive programmes.
Who it's for
- Predictive maintenance engineers looking to apply AI to their condition-monitoring work.
- Reliability engineers who want to move from statistical analysis to data-driven prognostics.
- Industrial data scientists seeking solid grounding in failure modes and maintenance practice.
- Vibration analysts aiming to automate diagnosis with machine learning.
- Asset management leads responsible for maintenance strategy and budgets.
- Maintenance 4.0 consultants advising plants on predictive programmes.
- Engineering or science graduates looking to enter the wind energy sector.
What you'll work with
- ISO 13374 (condition monitoring and diagnostics of machines — data processing and communication)
- ISO 17359 (condition monitoring and diagnostics of machines — general guidelines)
- ISO 20816 (measurement and evaluation of machine vibration)
- ISO 55000 (asset management)
- SAE JA1011 (evaluation criteria for RCM processes)
- UNE-EN 13306 (maintenance terminology)
- ISO 14224 (collection and exchange of reliability and maintenance data)
- IEC 60812 (failure modes and effects analysis, FMEA)
- Python scikit-learn
- MATLAB Predictive Maintenance Toolbox
- SAP PM
- IBM Maximo
- Azure IoT
12 modules
1.1Corrective, preventive and predictive maintenance1.2Condition-based maintenance1.3P-F curve and detection interval1.4RCM according to SAE JA10111.5Asset criticality analysis1.6MTBF, MTTR and availability indicators1.7Maintenance and downtime costs1.8Asset management according to ISO 550001.9Maturity and Maintenance 4.01.10Case study: criticality matrix for a plant
2.1FMEA and FMECA2.2Fault tree analysis2.3Weibull distribution2.4Failure rate and the bathtub curve2.5Survival analysis2.6Censored failure data2.7OREDA databases2.8Failure modes in bearings and gears2.9Electrical faults in motors2.10Practical exercise: Weibull analysis of pump failures
3.1Vibration analysis3.2Spectrum, envelope and order analysis3.3Infrared thermography3.4Oil and particle analysis3.5Ultrasound and acoustic emission3.6Motor current signature analysis (MCSA)3.7Wireless IIoT sensors3.8Selecting the technique by failure mode3.9ISO 10816 and ISO 20816 standards3.10Lab: diagnosing misalignment through vibration
4.1Sources: SCADA, CMMS and historians4.2Work orders as labels4.3Synchronising process data4.4Feature engineering on signals4.5Frequency-domain features4.6Class imbalance in failures4.7Time windows and data leakage4.8Synthetic data and fault simulation4.9Data quality and cleaning4.10Practical exercise: training dataset from a CMMS
5.1Statistical and control methods5.2Isolation Forest and One-Class SVM5.3Autoencoders for anomalies5.4Reconstruction-based models5.5Adaptive thresholds5.6Contextual and collective anomalies5.7Reducing false alarms5.8Evaluation without labels5.9Explaining alerts5.10Lab: anomaly detector for compressors
6.1Supervised fault classification6.2Convolutional networks on spectrograms6.3Transfer learning between machines6.4Hybrid physics-data models6.5Few-shot diagnosis6.6Multi-sensor fusion6.7CWRU and PRONOSTIA benchmark datasets6.8Cross-validation by asset6.9Interpretability for technicians6.10Practical exercise: classifying bearing defects
7.1Health indicators7.2Degradation models7.3RUL estimation with regression7.4LSTM and temporal models7.5Particle filters7.6Uncertainty in prognostics7.7C-MAPSS turbofan dataset7.8Prognostic metrics7.9Fleet-level prognostics7.10Lab: engine RUL with C-MAPSS
8.1From prognosis to work order8.2Interval optimisation8.3Planning under production constraints8.4Spare parts management8.5Opportunity cost and risk8.6Opportunistic maintenance8.7Integration with SAP PM and Maximo CMMS8.8Alert prioritisation8.9Policy simulation8.10Case study: optimal policy for a pump fleet
9.1Edge-cloud architecture9.2Industrial IIoT platforms9.3Edge inference9.4MLOps for maintenance models9.5Drift monitoring9.6OT/IT integration9.7IEC 62443 cybersecurity9.8Scaling to multiple plants9.9Costing the solution9.10Practical exercise: deploying a model on an industrial gateway
10.1Wind turbines and gearboxes10.2Gas and steam turbines10.3Railway rolling stock10.4Aerospace and MSG-310.5Machine tools and spindles10.6Pumps and compressors in petrochemicals10.7Power transformers10.8Vehicle fleets10.9Documented return on investment10.10Workshop: proposal for a wind farm
11.1Roles in the reliability team11.2Training maintenance technicians11.3Integration into existing processes11.4Managing expert knowledge11.5Programme success indicators11.6Contracting predictive services11.7Safety and ergonomics in inspections11.8AI regulation and liability11.9Implementation roadmap11.10Case study: implementation plan for a factory
12.1Selecting the asset and scope12.2Criticality and failure mode analysis12.3Data acquisition and preparation12.4Detection and diagnosis models12.5Remaining useful life estimation12.6Integration into decision-making12.7Economic evaluation12.8Conclusions and scaling12.9Written thesis report12.10Master's Thesis: defence before a panel
How you'll learn
100% online training 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 fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python scikit-learn, MATLAB Predictive Maintenance Toolbox, SAP PM, IBM Maximo and Azure IoT, in free, academic or evaluation versions. Testing is studied through real procedures: vibration analysis on bearing test rigs, model validation with fleet data and pilots on production lines. Assessment is continuous for each module, plus the final Master's Thesis.
Standards covered: ISO 13374, ISO 17359, ISO 20816, ISO 55000, SAE JA1011, UNE-EN 13306, ISO 14224, IEC 60812.
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
- 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.
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 Python scikit-learn, MATLAB Predictive Maintenance Toolbox, SAP PM, IBM Maximo and Azure IoT, 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), 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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