Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI for Predictive Maintenance in Industrial IoT shows how machine learning turns the sensor data streaming off industrial equipment into foresight. You learn to work with IIoT sensor and vibration data, engineer features that reveal wear, and build models that predict failure and estimate remaining useful life — so maintenance happens before breakdown, not after. The course covers deploying models to the edge and connecting predictions to real maintenance and operations decisions. You finish able to reason about an AI predictive-maintenance solution. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to predictive maintenance in Industrial IoT — using sensor data and machine learning to predict equipment failure and optimise industrial operations.
📋 Course Objectives
1. Work with IIoT sensor and vibration data.
2. Engineer features that reveal equipment wear.
3. Build failure-prediction models.
4. Estimate remaining useful life.
5. Deploy models and act on predictions.
👥 Who Should Enroll?
• Manufacturing and reliability engineers
• Industrial IoT and data teams
• Operations and maintenance professionals
• Students of industrial AI
🚀 Key Learning Outcomes
• The ability to apply predictive maintenance.
• A reduced-downtime perspective.
• An Industrial-IoT project.
• 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
Sensing the Asset Correctly
• Vibration, acoustic, thermal and current signature sensing, and what each detects
• Sensor placement, mounting and the sampling rates faults actually require
• Industrial protocols: OPC UA, Modbus and the realities of brownfield plants
Architecture and Data Movement
• Edge versus cloud processing under bandwidth and latency constraints
• Time-series storage, compression and retention for high-rate signals
• Time synchronisation across assets, without which correlation is meaningless
Signal-Based Fault Detection
• Spectral analysis, envelope detection and bearing fault frequencies
• Condition indicators and thresholds grounded in machinery standards
• Anomaly detection where labelled failures are scarce or absent
Estimating Remaining Useful Life
• Degradation modelling and RUL estimation with calibrated uncertainty
• Run-to-failure data scarcity and using survival analysis with censored records
• Translating a probability of failure into a maintenance decision
Deployment on the Plant Floor
• CMMS integration so predictions become scheduled work orders
• Operator trust, false alarms and the cost of an unnecessary shutdown
• Measuring benefit in avoided downtime rather than model accuracy
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
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