
AI for Predictive Maintenance in Industrial IoT
Enabling Intelligent Asset Reliability through AI-Driven Predictive Maintenance in Industrial IoT
Skills you will gain:
About Program:
This workshop provides a comprehensive understanding of AI-driven predictive maintenance in Industrial IoT, focusing on data engineering, advanced machine learning models, and deployable edge intelligence to enhance reliability, reduce downtime, and optimize industrial operations
Aim: To equip participants with the knowledge and practical skills to implement AI-driven predictive maintenance strategies in Industrial IoT environments for improved reliability and efficiency.
Program Objectives:
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To introduce AI and Industrial IoT concepts for predictive maintenance.
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To understand sensor data collection and real-time monitoring.
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To explore machine learning for fault and failure prediction.
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To develop skills in industrial data analysis.
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To demonstrate AI-based smart maintenance solutions.
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To highlight applications for reducing downtime and improving efficiency.
What you will learn?
📅Day 1: Data Engineering & IIoT Signal Processing
- Evolution from preventive to AI-driven predictive maintenance
- Industrial data acquisition and sensor fusion
- Feature extraction using FFT and Wavelet Transform
- Hands-on: Digital Signal Processing for Fault Detection
📅Day 2: Deep Learning for RUL Estimation
- RNNs, LSTMs, and GRUs for degradation modeling
- Remaining Useful Life (RUL) prediction
- Transformer-based attention mechanisms
- Hands-on: Predictive Modeling with LSTM Networks
📅Day 3: Edge AI, Optimization & Explainability
- Edge computing and TinyML deployment
- Unsupervised anomaly detection with Autoencoders and Isolation Forests
- Explainable AI using SHAP and LIME
- Hands-on: Unsupervised Fault Detection & XAI
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!

Intended For :
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ndustry professionals in IIoT and smart manufacturing
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Maintenance and reliability engineers
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AI/ML and data science professionals
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IoT and embedded systems developers
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Researchers, academicians, and PhD scholars
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Students in AI, IoT, electronics, and related fields
Career Supporting Skills
Program Outcomes
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Ability to understand and apply AI techniques for predictive maintenance.
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Skills to analyze industrial IoT data for fault detection and prediction.
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Knowledge of integrating AI models with IoT-based systems.
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Capability to design data-driven maintenance strategies.
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Improved decision-making for reducing downtime and enhancing efficiency.
