Industrial Training on IIoT 4.0 and Machine Learning

Module 1: Introduction to Industry 4.0 & IIoT

Objectives:

• Understand Industry 4.0 concepts, smart factories, and cyber-physical systems.
• Identify IIoT use cases in manufacturing, utilities, logistics, etc.

Key Topics:

• Evolution from Industry 1.0 to 4.0
• Components: CPS, Cloud, Edge, Big Data, AI
• What is IIoT? Difference between IoT and IIoT
• Industrial use cases: predictive maintenance, OEE monitoring, energy optimization, asset tracking

Moodle items:

• Resource: PDF/Presentation – “Industry 4.0 & IIoT Fundamentals”
• URL: 1–2 short videos (YouTube)
• Activity: Quiz 1 (MCQs on concepts)
• Forum: “Share an IIoT use case from your industry”

Module 2: IIoT Architecture, Protocols & Platforms

Objectives:

• Explain IIoT reference architectures.
• Understand industrial communication protocols.

Key Topics:

• IIoT reference architecture (sensor → edge → gateway → cloud → dashboard)
• Industrial connectivity & protocols:
  • MQTT, HTTP/REST, CoAP
  • OPC UA, Modbus basics
• Edge vs Cloud vs On-prem deployment
• Overview of common IIoT platforms (open-source & cloud)

Moodle items:

• Page/Book: “IIoT Architecture & Protocols Overview”
• Assignment: Draw your organization’s current architecture and propose an IIoT-enabled version (upload PDF/image).
• Quiz 2: Protocols & architecture.

Module 3: Sensors, Edge Devices & Data Acquisition

Objectives:

• Understand field devices, sensors, and edge hardware.
• Learn how data is collected and streamed.

Key Topics:

• Types of industrial sensors: temperature, vibration, pressure, flow, current, proximity, etc.
• Microcontrollers & edge devices (generic explanation: PLCs, Raspberry Pi, ESP32, industrial gateways)
• Data acquisition basics: sampling rate, resolution, signal conditioning
• Streaming data to broker/cloud (MQTT publisher–subscriber pattern)
• Data logging: CSV, time-series DB, dashboards

Moodle items:

• Resource: PDF with sample wiring diagrams or conceptual diagrams
• URL/Lab sheet: Simple “MQTT publisher/subscriber” demo (even if simulated)
• Assignment: Short report – “Identify 5 important sensors for your plant/use case and justify why”.

Module 4: Data Engineering for IIoT Analytics

Objectives:

• Prepare raw IIoT data for ML.
• Understand handling of time-series and event data.

Key Topics:

• Data types in IIoT: time-series, events, alarms, logs
• Data cleaning: missing data, noise, outliers
• Feature extraction/engineering from sensor data:
  • Statistical features: mean, RMS, peak, variance, rolling window stats
  • Time-lag features, moving averages, aggregation
• Basic data pipelines: ingest → clean → transform → store
• Tools overview: Python (pandas), Jupyter, basic CSV operations

Moodle items:

• Resource: Jupyter notebook/PDF: “Basic preprocessing of IIoT sensor data”
• Assignment: Mini-task – Upload a CSV (provided) after cleaning & adding 2–3 features.
• Quiz 3: Data preprocessing & time-series basics.

Module 5: Machine Learning for IIoT Applications

Objectives:

• Train basic ML models for industrial use cases.
• Interpret model outputs for decision-making.

Key Topics:

• Quick recap: supervised vs unsupervised learning
• Common ML tasks in IIoT:
  • Predictive maintenance (remaining useful life, breakdown prediction)
  • Anomaly detection (faults, leakage, abnormal vibration)
  • Quality prediction (pass/fail, defect classification)
• Algorithms (conceptual, not super heavy maths):
  • Regression: Linear/Random Forest Regressor
  • Classification: Logistic Regression, Random Forest, XGBoost (intro)
  • Clustering: k-Means for anomaly detection
• Model evaluation metrics: accuracy, precision, recall, F1, ROC-AUC, confusion matrix
• Interpreting results in an industrial context (reducing downtime, cost savings).

Moodle items:

• Resource: Notebook or PDF – sample ML pipeline on sensor dataset
• Quiz 4: ML concepts & metrics
• Assignment: Build a simple ML model (or concept report) using provided dataset (predict normal/faulty condition).

Module 6: Edge AI & Real-time Deployment Concepts

Objectives:

• Understand deployment challenges in real-time industrial environments.
• Introduce Edge AI and lightweight models.

Key Topics:

• Why Edge AI? Latency, bandwidth, privacy, reliability
• Deploying models on edge devices (concept level, not deep coding):
  • Model compression basics (quantization, pruning – high level)
  • Real-time inference pipeline
• Integration with dashboards and alert systems
• Cybersecurity basics in IIoT & ML systems

Moodle items:

• Page: “From Prototype to Production: Deploying ML in IIoT Environments”
• Forum: Discussion – “Cloud vs Edge: What is realistic for your industry?”
• Short assignment: Design a conceptual architecture diagram showing where the ML model will run.

Module 7: Capstone Project & Assessment

Objectives:

• Apply the entire pipeline from IIoT concept to ML model & deployment plan.
• Present a practical, industry-oriented solution.

Capstone Task (example):
Learners must choose one industrial problem, e.g.:

• Predictive maintenance of a motor/pump
• Energy consumption optimization in a small plant
• Temperature/humidity monitoring with anomaly alerts
• Production line defect detection concept

Deliverables (for Moodle submission):

1. Architecture Diagram – Sensors → Edge → Gateway → Cloud → Analytics.
2. Data & ML Plan – What data, features, and ML algorithm will be used?
3. Demo/Prototype (optional if time) – Notebook, screenshots, or simulation.
4. Business Impact Note – 1–2 pages on cost saving, reliability, safety, or efficiency improvement.

Moodle items:

• Assignment (Project Report + Files Upload)
• Activity: Online Presentation (via BigBlueButton/Zoom link) or offline evaluation
• Feedback form (Questionnaire) for course evaluation.

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