Programme Parameters
Advanced Postgrad
3 Days (4.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
The AI‑First Predictive Maintenance for Legacy Substations course teaches professionals to apply AI and machine learning for modernizing substation maintenance. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
The AI‑First Predictive Maintenance for Legacy Substations course teaches professionals to apply AI and machine learning for modernizing substation maintenance.
📋 Course Objectives
1. Translate AI Enablement theory into practical, reproducible analysis.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• A portfolio-grade AI Enablement deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Day 1 – Foundations & Data Enablement
Identify asset types and failure modes across transformers, breakers, relays, CT/PT, batteries • Integrate SCADA, DNP3, Modbus, IED logs, DGA, thermography, vibration and PQ data • Prepare unified time‑series datasets and compute a baseline Asset Health Index
Day 2 – Diagnostics, RUL & Risk
Develop anomaly‑detection models using adaptive baselines, one‑class SVMs and auto‑encoders • Create condition‑diagnostic pipelines combining DGA, partial discharge patterns and power quality correlation • Build survival‑analysis and gradient‑boosted RUL models, calibrate with SHAP explanations and cost‑sensitive metrics
Day 3 – Deployment & Operations
Construct edge‑to‑cloud pipelines for real‑time feature streaming, model serving and drift management • Design human‑on‑the‑loop workflows: triage, suppression rules, escalation and user‑experience dashboards • Integrate alerts with CMMS/APM to generate work orders, manage spares, schedule outages and track SLA compliance
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | scikit-learn |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Azure AI |
| Covered Tool / Platform | Docker |
| Covered Tool / Platform | SCADA/DNP3 connectors |
| Covered Tool / Platform | CMMS APIs |
| Covered Tool / Platform | SHAP |
Frequently Asked Questions
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