Predictive Maintenance with Machine Learning
الصيانة التنبؤية باستخدام تعلم الآلة
- Next date
- Available in-house
- Fees from
- —per delegate
- Formats
- In-house training0 sessions
- Duration
- 5 daysCertificate
Overview
Most rotating equipment gives warning before it fails: vibration rises, temperatures drift, efficiency falls and lubricant condition changes. Plants already collect much of this data in their historians and condition monitoring systems, but often use it only for alarms. Machine learning makes it possible to detect subtle patterns earlier, estimate remaining useful life and focus maintenance where it matters. This course shows maintenance and reliability professionals how predictive maintenance models are built and used, without requiring them to become data scientists. Participants learn how to frame a predictive maintenance problem, prepare sensor and maintenance data, choose between anomaly detection, classification and remaining life models, and judge whether a model is good enough to act on. Hands-on sessions use Python notebooks on real pump and compressor data, and the final part covers deployment, integration with CMMS and the work processes needed to turn predictions into avoided failures.
Objectives
- Explain how predictive maintenance differs from preventive and condition-based maintenance
- Frame a predictive maintenance problem around failure modes
- Prepare and clean sensor, historian and work order data
- Build anomaly detection and failure classification models
- Estimate remaining useful life and judge model accuracy
- Plan deployment and integration with CMMS and work processes
- Build a value case for predictive maintenance
Who should attend
Reliability and maintenance engineers, condition monitoring specialists, rotating equipment engineers and digital team members. Basic spreadsheet skills are needed; prior programming experience is helpful but not required.
Training methodology
About half of the course is hands-on. Participants use prepared Python notebooks to explore historian data, engineer features such as vibration trends and efficiency deviation, and train simple models step by step. Each technical topic starts with a reliability question, for example how early a bearing failure can be detected. Case studies from refineries and gas plants show what worked and what did not. The course ends with teams drafting a predictive maintenance pilot plan for one of their own assets.
Programme outline
Day1Maintenance Strategy and Data
- Reactive, preventive, condition-based and predictive maintenance
- Failure modes and the P-F curve
- Data sources: historians, vibration, oil analysis, CMMS
- Data quality problems in plant data
- Introduction to Python notebooks
- Practical exercise: exploring pump historian data
- Discussion: where failures give warning
Day2Preparing Data and Features
- Time series cleaning and resampling
- Linking work orders to sensor data
- Feature engineering for rotating equipment
- Operating modes and normalisation
- Labelling failures
- Practical exercise: building a feature set
- Practical exercise: visualising degradation
Day3Anomaly Detection and Classification
- Statistical baselines and thresholds
- Unsupervised anomaly detection
- Supervised failure classification
- Training, testing and cross-validation
- Precision, recall and false alarms
- Practical exercise: compressor anomaly model
- Interpreting model outputs
Day4Remaining Useful Life
- Degradation models
- Regression approaches to RUL
- Survival analysis basics
- Uncertainty and confidence intervals
- Choosing the right approach for the asset
- Practical exercise: estimating remaining life
- Case study: gas turbine monitoring
Day5Deployment and Value
- From notebook to production
- Integration with CMMS and work processes
- Alert management and operator trust
- Measuring benefits and avoided failures
- Scaling across an asset fleet
- Team exercise: predictive maintenance pilot plan
- Final assessment
Dates and fees
There are no scheduled dates for this course at the moment. Request it as in-house training, or ask us about the next date.
Certificate
Every delegate who attends the full programme receives a Harfard Technology certificate of completion stating the course title, duration, dates and venue.
Frequently asked questions
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