Course Description
This hands-on course teaches engineers and analysts to design, build, and validate predictive maintenance systems using Python — the industry-standard language for data science and machine learning. Participants work with sensor data, build machine learning models, and create dashboards to predict equipment failure.
The course is practical and code-based: participants integrate condition monitoring and sensor data into machine learning pipelines, detect anomalies, estimate remaining useful life, and build scalable, CMMS-compatible solutions. It bridges reliability engineering with practical Python programming, suitable for those ready to build predictive maintenance capability themselves rather than only understand it conceptually.
What you will achieve
By the end of this training course, participants will be able to:
- Use Python and its data science libraries (pandas, scikit-learn) for maintenance data
- Load, clean, and prepare industrial sensor data
- Engineer features from time-series sensor signals
- Build and train machine learning models for failure prediction
- Apply anomaly detection to condition monitoring data
- Estimate remaining useful life with Python models
- Validate model performance with appropriate metrics
- Build dashboards and CMMS-compatible outputs
How the course is delivered
This is a hands-on, code-based course. Participants work directly in Python with industrial and synthetic sensor datasets, building predictive maintenance pipelines step by step. Exercises cover data preparation, modelling, validation, and dashboarding.
Basic programming familiarity is helpful but core Python concepts are introduced. The emphasis is on building working predictive maintenance solutions.
Designed for
This training course is ideal for:
- Reliability and maintenance engineers ready to code
- Data-focused maintenance professionals
- Industrial data scientists and analysts
- Engineers building predictive maintenance in-house
- Technical staff with basic programming interest
Daily programme
- Python and data science libraries overview
- Loading and exploring sensor data with pandas
- Data cleaning and preparation
- Working with time-series data
- Feature engineering from sensor signals
- Time and frequency domain features
- Labelling failure data
- Building the training dataset
- Machine learning with scikit-learn
- Training failure prediction models
- Classification and regression for PdM
- Model evaluation and metrics
- Anomaly detection in Python
- Remaining useful life estimation
- Handling imbalanced failure data
- Model validation and tuning
- Building maintenance dashboards
- CMMS-compatible outputs
- Deploying and scaling models
- Capstone exercise and review
Certification & accreditation
BII Certificate of Completion
BII Certificate of Completion Upon successful completion, participants receive a BII Development Institute Certificate of Completion with a unique reference code that is independently verifiable. Our certificates are recognised internationally and reflect successful completion of your chosen programme.
