Powering Industry 4.0 in Energy: RoboDK Cells, IoT Twins & ML Analytics

This 3-day workshop, “Powering Industry 4.0 in Energy: RoboDK Cells, IoT Twins & ML Analytics,” walks participants through a complete digital pipeline for modern energy operations.

To equip participants with a practical, end-to-end workflow for Industry 4.0 in the energy sector—linking virtual robot cells, IoT-style digital twins, and ML analytics in Google Colab, without needing physical hardware or proprietary platforms.

• Understand how robotic cells, IoT digital twins, and ML analytics fit together in an Industry 4.0 energy context.

• Model and simulate a simple robot arm and pick–place cell in Python, including workspace plotting and basic cycle-time/utilization calculations.

• Design and implement a basic IoT-style digital twin for a robot cell, with streaming telemetry, time-series storage, dashboards, and alert logs in Colab.

• Engineer features and train lightweight anomaly detection models on simulated telemetry for quality, throughput, and maintenance use-cases.

• Deploy model logic inside the twin, triggering alerts and recommended actions and summarizing their effect using operator-facing KPIs.

📅 Day 1 – Smart Robotics Cells (Virtual Robot in Google Colab)

• Industry 4.0 context: automating energy assets (substations, PV inverters, turbine components) and where robotic cells fit.
• Robot & cell fundamentals: joints, DOF, reach, payload, cell layout (robot, fixtures, conveyor, safety area).
• Offline programming (OLP) concepts: waypoints, paths, TCP, simple motion and cycle-time thinking (no real robot/software).
• Integration & logic: I/O signals (sensors, grippers), basic state machine (idle → running → fault), simple utilization and cycle-time analysis.
• Hands-on (Colab): Build a simple virtual robot arm in Python (2–3 link planar model), plot its workspace, simulate a pick–place cycle with virtual I/O logic, and compute a basic cycle-time & utilization report using numpy, matplotlib, and pandas in Google Colab.

📅 Day 2 – IoT Digital Twins & Event-Driven Ops (Simulated in Colab)

• Architecture: conceptual edge-to-cloud pipeline (device → gateway → protocol → time-series store → dashboard/API) using Python objects instead of real hardware.
• Digital twin modeling: define a robot cell twin in code (asset hierarchy, states like idle/running/fault, simple KPIs such as cycle time, basic OEE view, energy per unit).
• Streaming analytics: simulate time-stamped telemetry (cycle time, positions, temperatures, currents), store it as a time-series table, and apply rule-based event detection (cycle overrun, temperature exceedance).
• Reliability & security (conceptual): device identity, basic idea of secure zones, audit-style logging of state changes and alerts in data.
• Hands-on (Colab): Implement a RobotCellTwin class in Python, simulate streaming telemetry into a pandas DataFrame (acting as a TSDB), generate dashboard-style plots in matplotlib, and build an alert log for rule-based events like cycle overruns and high temperature, all within Google Colab.

📅 Day 3 – ML Analytics for Quality, Throughput & Maintenance (End-to-End in Colab)

• Data prep: load the simulated telemetry from Day 2, perform feature engineering (rolling stats on temperature/cycle time, alert counts), and handle simple label imbalance between normal and abnormal cycles.
• Predictive maintenance: frame normal vs abnormal behavior, introduce anomaly detection and basic RUL intuition using the simulated data.
• Quality & throughput analytics: relate cycle-time stability, temperature patterns, and alerts to quality/throughput KPIs and simple traceability from cycle ID → process data → outcome label.
• MLOps & operator view (conceptual): “deploy” model logic as a Python function inside the twin, trigger alarms and recommended actions, and summarize impact via KPIs.
• Hands-on (Colab): Train a lightweight anomaly detection model (rule-based or sklearn IsolationForest/OneClassSVM) on the simulated telemetry in Google Colab, use it to score incoming cycles from the twin, generate maintenance/quality alerts with recommended actions, and export a KPI snapshot estimating downtime avoided and energy-per-unit improvement.

This workshop is ideal for:

• Engineers & practitioners in energy, manufacturing, mechatronics, robotics, or industrial automation

• Data scientists & ML engineers wanting applied use-cases in Industry 4.0 and energy assets

• Students, researchers, and faculty in EE/ME/IE/CS looking for hands-on digital twin and analytics experience

• Process, operations, and maintenance teams exploring analytics, digital twins, and robotics for substations, PV plants, or turbines

• A virtual robot cell workflow in Colab: simple kinematics, workspace visualization, pick–place simulation, and cycle-time/utilization insights.

• A Python-based IoT digital twin that simulates, stores, visualizes, and raises alerts on robot cell telemetry using pandas and matplotlib.

• Embedded ML-style analytics (rule-based or sklearn anomaly detection) that detect abnormal cycles, trigger maintenance/quality actions, and summarize impact with operator-ready KPI snapshots (e.g., downtime avoided, energy-per-unit improvements)