Master’s in AI-Based Predictive Maintenance Engineering
Online master's programme of 1,900 hours in AI-based predictive maintenance: 12 modules covering maintenance data, feature engineering, fault diagnosis, asset anomaly detection, remaining useful life prognosis, hybrid models and digital twins, deployment and MLOps, maintenance decision optimisation and generative AI governance, with an assigned technical tutor.
Master’s in AI-Based Predictive Maintenance Engineering — studying from the United States
Data science, artificial intelligence and simulation are central to American industry, with major concentrations in the San Francisco Bay Area, Seattle, New York and Boston. Engineering organizations increasingly rely on machine learning, digital twins and high-performance computing to accelerate design, testing and operations. Employers range from large technology companies to aerospace, automotive and energy firms that apply analytics to complex systems. Demand is strong for engineers who combine domain knowledge with modeling, statistics and software skills.
Fees for the United States: US$9,970 (US Dollars), with instalments and employer sponsorship available.
Relevant bodies in the United States
Typical roles
Questions from the United States
Which US bodies relate to data science, AI and simulation engineering?
There is no single licensing body for data science or AI in the US. Relevant professional organizations include the IEEE Computer Society, the Association for Computing Machinery (ACM), and the Society for Industrial and Applied Mathematics (SIAM). Federal guidance such as the NIST AI Risk Management Framework shapes practice. SEIUM's qualifications are private and not accredited by a national body, so recognition is decided by employers.
How do US employers in data and AI regard private online postgraduate qualifications?
US tech firms, financial institutions and research labs generally hire on the basis of portfolios, technical interviews, relevant experience and sometimes advanced degrees. A private online qualification can show structured study, but it is not a standard hiring requirement and each employer weighs it differently. Public projects, code repositories and measurable results tend to matter most.
Is it realistic to study data and AI from the US around a full-time job?
Yes, the programmes are fully online and taught in English, so you can study from home without a student visa. The workload is large: about 1,900 hours for a master's, 800 hours for a postgraduate diploma, and 300 hours for a specialist course. An assigned technical tutor supports you. Fees may be paid in instalments, and employer sponsorship is sometimes available, which you should confirm with your employer.
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
Prepare maintenance data by combining sensors and the CMMS.
Build health indicators from asset signals.
Develop fault diagnosis models with machine learning.
Estimate remaining useful life with uncertainty quantification.
Integrate physical and data-driven models into digital twins.
Deploy predictive solutions using MLOps practices.
Turn prognoses into optimal maintenance decisions.
Who it's for
- Predictive maintenance engineers moving from condition monitoring to AI.
- Asset data scientists working with SCADA, vibration and CMMS data.
- Reliability engineers who want data-driven prognostics.
- Industrial MLOps engineers deploying models across asset fleets.
- Asset management leads responsible for maintenance strategy and budgets.
- Maintenance 4.0 consultants advising on digital transformation.
- Engineering or science graduates aiming to move into wind energy.
What you'll work with
- ISO 17359 (condition monitoring and diagnostics of machines: general guidelines)
- ISO 13374-1 (condition monitoring data processing, communication and presentation)
- ISO 13381-1 (condition monitoring and diagnostics of machines: prognostics)
- Regulation (EU) 2024/1689 (AI Act) (European Artificial Intelligence Regulation)
- ISO 55001 (asset management systems)
- IEC 60599 (interpretation of dissolved gas analysis in mineral-oil-filled equipment)
- ISO/IEC 42001 (artificial intelligence management systems)
- ISO 20816-1 (measurement and evaluation of machine vibration)
- Python with scikit-learn and PyTorch
- MATLAB Predictive Maintenance Toolbox
- MLflow
- Azure Machine Learning
- Grafana
12 modules · 1900 h
1.1Maintenance strategies and their evolution1.2Condition-based maintenance according to ISO 173591.3Data sources: sensors, CMMS and SCADA1.4Mapping failure modes to measurable variables1.5Asset criticality and prioritisation1.6Digital maturity of maintenance1.7Success stories in industry and energy1.8Common mistakes in predictive projects1.9Building the business case1.10Case study: selecting assets for a predictive pilot
2.1Extracting CMMS history2.2Free text in work orders2.3Natural language processing of breakdowns2.4Aligning events with signals2.5Labelling failures and pre-failure windows2.6Handling missing data2.7Imbalanced failure data2.8Operating condition data2.9Information leakage in labelling2.10Lab: labelling failures from work orders
3.1Band-based vibration features3.2Temperature and current indicators3.3Process and load features3.4Temporal aggregates and trends3.5Physics-based features3.6Composite health indicators3.7Selection by monotonicity and prognosability3.8Normalisation by operating regime3.9Feature automation3.10Practical: health indicator for a turbomachine
4.1Tree-based classifiers and gradient boosting4.2Support vector machines4.3Convolutional networks on spectrograms4.4Multi-class fault diagnosis4.5Learning with few labels4.6Transfer learning between machines4.7Validation by asset and by time4.8Confusion matrices and costs4.9Interpretability of the diagnosis4.10Lab: bearing fault diagnosis with a CNN
5.1Normal behaviour models5.2Model residual regression5.3Autoencoders on SCADA data5.4Isolation Forest under operating conditions5.5Thresholds and alarm persistence5.6Separating process anomalies from equipment anomalies5.7Fusion of multiple detectors5.8Reducing false alarms5.9Validation with experts5.10Workshop: normal behaviour model of a wind turbine
6.1Formulating the RUL problem6.2Survival models and Cox regression6.3Stochastic degradation processes6.4Direct RUL regression6.5LSTMs and CNNs for RUL6.6The C-MAPSS dataset and benchmarks6.7Uncertainty quantification in RUL6.8Bayesian models and bootstrapping6.9Prognostic metrics6.10Practical: RUL on the C-MAPSS dataset
7.1Rotating machinery and electric motors7.2Transformers and dissolved gas analysis7.3Pumps and compressors7.4Wind turbines and SCADA7.5Railway rolling stock7.6Oil and gas assets7.7Civil infrastructure and structures7.8Vehicle fleets7.9HVAC equipment7.10Case study: dissolved gas analysis with machine learning
8.1Physical degradation models8.2Physics-informed neural networks8.3Calibrating models with data8.4Digital twins for maintenance8.5Failure simulation to generate data8.6Synthetic data and augmentation8.7Surrogate models of degradation simulations8.8Validating hybrid models8.9Scaling to fleets8.10Lab: hybrid wear model
9.1Edge and cloud architectures9.2Periodic inference pipelines9.3Integration with the CMMS and notification generation9.4Monitoring model drift9.5Retraining on new failures9.6Managing models per asset9.7Commercial industrial platforms9.8OT cybersecurity in predictive solutions9.9Solution scalability9.10Workshop: inference pipeline integrated with the CMMS
10.1From prognosis to decision10.2Prescriptive maintenance and action recommendations10.3Constrained schedule optimisation10.4Spare parts management with forecasts10.5Cost-benefit analysis of alerts10.6Decision theory under uncertainty10.7Reinforcement learning in maintenance10.8Programme value indicators10.9Communicating with senior management10.10Practical: optimising interventions with RUL forecasts
11.1LLM assistants for maintenance technicians11.2Retrieval of manuals and maintenance histories11.3Generating breakdown reports11.4Hallucination risks in critical environments11.5The AI Act and human oversight11.6AI management according to ISO/IEC 4200111.7Accountability and traceability of decisions11.8Change management in technical teams11.9Ethics and employment11.10Case study: RAG assistant for equipment manuals
12.1Defining the asset and the problem12.2Data preparation12.3Features and health indicators12.4Diagnosis and prognosis models12.5Integration into decision-making12.6Deployment architecture12.7Technical and economic evaluation of the programme12.8Conclusions and scaling plan12.9Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
100% online training on the SEIUM campus, with downloadable materials, 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 with scikit-learn and PyTorch, MATLAB Predictive Maintenance Toolbox, MLflow, Azure Machine Learning and Grafana, in free, academic or evaluation versions. Real procedures are studied, such as engine remaining useful life prognosis with the C-MAPSS dataset, normal behaviour models with SCADA data and transformer diagnosis by dissolved gas analysis. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: ISO 17359, ISO 13374-1, ISO 13381-1, Regulation (EU) 2024/1689 (AI Act), ISO 55001, IEC 60599, ISO/IEC 42001, ISO 20816-1.
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 with scikit-learn and PyTorch, MATLAB Predictive Maintenance Toolbox, MLflow, Azure Machine Learning and Grafana, 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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