Predictive Maintenance with AI: Advanced Implementation
Program outline
Program Structure
- Stage 1 — Equipment data audit and sensor gap analysis
- Stage 2 — Feature engineering for vibration, temperature, and current signals
- Stage 3 — Model training, validation, and failure mode mapping
- Stage 4 — Integration with SAP PM and IBM Maximo (case studies)
- Stage 5 — Deployment, monitoring, and retraining protocols
- Stage 6 — Capstone: full implementation plan for a participant-selected asset
The capstone is reviewed by a practicing reliability engineer, not just an instructor, giving feedback grounded in current field conditions.
About this program
Predictive maintenance sounds straightforward until you are the one responsible for making it work on a 15-year-old CNC line with inconsistent sensor coverage.
The actual implementation challenges
This program focuses on the problems that arise after a pilot project ends: data drift, sensor failure, model retraining schedules, and integration with existing CMMS platforms. You will work through three full deployment scenarios, each with different equipment types and data quality constraints. The goal is to leave with a repeatable process, not just familiarity with the concept.
Technical depth and tooling
Sessions cover time-series analysis using Python libraries including pandas and scikit-learn, as well as integration patterns for connecting model outputs to maintenance work order systems. You do not need to be a software developer, but you should be comfortable reading and modifying Python scripts. The course provides templates and annotated notebooks for each major task.
Realistic expectations
A well-implemented predictive maintenance system typically reduces unplanned downtime over 12 to 18 months, but results depend heavily on data availability and organizational follow-through. This course is honest about those dependencies.
- Time-series feature engineering for rotating equipment
- Model selection and validation for imbalanced failure datasets
- CMMS integration and alert threshold calibration
- Retraining pipelines and monitoring in production