NZAI-AI-205
Predictive Maintenance with AI for Energy Equipment
Design and deploy AI-driven predictive maintenance for turbines, compressors, pumps, transformers, and balance-of-plant. Learn how to turn vibration, temperature, oil, acoustic, and electrical signatures into actionable health indicators, failure predictions, and work orders.
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Industrial Signals
Vibration, temperature, oil/fine debris, ultrasound, electrical.
Failure Modes
Bearings, misalignment, imbalance, cavitation, insulation aging.
RUL & Anomalies
Health indices, survival/RUL models, autoencoder detection.
CMMS Workflow
Alerts → triage → work orders (SAP/Maximo-ready).
Overview
Move beyond time-based maintenance to AI-enabled reliability. You’ll build pipelines to ingest sensor streams, engineer spectral and statistical features, train anomaly/RUL models, and push clear, auditable recommendations to planners and field teams.
Curriculum
- Signals & sensing: accelerometers, thermography, oil analysis, ultrasound, partial discharge.
- Data engineering: resampling, denoising, spectral/cepstral features, envelopes & orders.
- Anomaly detection: isolation forests, autoencoders, PCA & multivariate thresholds.
- Supervised diagnostics: fault classification for bearings, imbalance, misalignment, cavitation.
- RUL & health indices: survival analysis, degradation models, composite health scoring.
- Model evaluation: precision/recall vs. cost-of-failure, lead-time, early-warning metrics.
- Alert design & triage: severity bands, nuisance reduction, human-in-the-loop review.
- Workflows & CMMS: integrating with SAP/Maximo; work-order creation & feedback loops.
- MLOps/governance: data drift, retraining cadence, model explainability & audit trails.
- Case study: compressor train PdM from sensor to work order and measured downtime saved.
What You Will Learn
By the end of this program, you will be able to:
- Ingest and prepare multi-sensor data for predictive maintenance at scale.
- Detect anomalies, classify failure modes, and estimate remaining useful life (RUL).
- Design alerting & triage that reduces false positives and speeds decision-making.
- Integrate PdM outputs into CMMS to drive prioritized, auditable work orders.
Benefits of Attending
- Reduced unplanned downtime and safer operations with earlier warnings.
- Repeatable feature sets, health indices, and alerting templates for quick rollout.
- Clear linkage from model outputs to maintenance actions and ROI tracking.
- Confidence to explain predictions to reliability engineers and leadership.
Practitioner Profile
Delivered by practitioners who have implemented PdM/condition monitoring across turbines, compressors, pumps, and transformers—integrated with EAM/CMMS and reliability frameworks.
Emphasis on practical deployment patterns, explainability, and measurable downtime reduction.