AI-First Predictive Maintenance for Legacy Substations

The AI-First Predictive Maintenance for Legacy Substations workshop teaches professionals to apply AI and machine learning for modernizing substation maintenance. Over three days, participants will learn to integrate data, build anomaly detection and RUL models, and deploy AI solutions with CMMS/APM integration, improving asset health, operational efficiency, and decision-making with measurable ROI.

This workshop aims to equip participants with the skills to implement AI-driven predictive maintenance for legacy substations, covering data integration, anomaly detection, RUL modeling, and deployment of end-to-end solutions for improved asset management and operational efficiency.

• Understand AI-driven predictive maintenance for legacy substations

• Learn data integration from SCADA, DNP3, Modbus, and other sources

• Build anomaly detection models and Remaining Useful Life (RUL) predictions

• Deploy AI solutions via edge-to-cloud pipelines for real-time monitoring

• Integrate predictive maintenance with CMMS/APM systems for efficient asset management

• Apply cybersecurity practices in AI solutions

• Implement scalable AI-powered maintenance systems for improved efficiency and ROI

📅 Day 1 – Foundations & Data Enablement

• Assets & failure modes: transformers, breakers, relays, CT/PT, batteries
• Data sources: SCADA/DNP3/Modbus, IED logs, DGA, thermography, vibration, PQ, CMMS
• Data readiness: time sync, drift, missing data, weak labels; feature basics (load cycles, gas ratios, wear indices)
• Governance & safety: change control, sign-off
• Hands-on: Build a unified asset + time-series dataset and a baseline Asset Health Index

📅 Day 2 – Diagnostics, RUL & Risk

• Anomaly detection: adaptive baselines, one-class/autoencoder
• Condition diagnostics: DGA + ML, PD patterns, PQ correlation
• RUL modeling: survival/Weibull, gradient boosting hazards, Bayesian updates
• Explainability & calibration: SHAP, reliability, cost-sensitive metrics
• Hands-on: Train anomaly + RUL models; output risk tiers with calibrated thresholds and a short model card

📅 Day 3 – Deployment & Operations

• Edge→cloud pipeline: ingest, streaming features, serving, drift, rollback
• Human-on-the-loop: triage, suppression rules, escalation, UX
• CMMS/APM integration: alert → work order, spares, outage windows, SLA
• Cybersecurity/compliance; value tracking: avoided failures, MTBF/MTTR, ROI
• Hands-on: Package the pipeline and demo “alert → work order” on a lightweight dashboard

• Engineers and maintenance managers in power distribution and substation operations

• Asset management professionals seeking to enhance predictive maintenance strategies

• Data scientists interested in applying AI/ML to industrial systems

• Professionals with a background in electrical engineering, industrial maintenance, or data science

• Individuals looking to implement AI-driven solutions in legacy substation systems

• Basic understanding of data handling and machine learning concepts is beneficial, but not mandatory

• AI-ready dataset for predictive maintenance of legacy substations

• AI-driven diagnostics and Remaining Useful Life (RUL) models for asset prioritization

• Practical experience in anomaly detection, RUL modeling, and risk management

• Deployment of end-to-end predictive maintenance pipelines (edge-to-cloud)

• Integration of AI solutions with CMMS/APM systems for efficient work order management

• Measurable improvements in asset health, operational efficiency, and ROI

• A production-ready system with human oversight and cybersecurity compliance