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Industrial Use Cases

Common industrial automation patterns and applications using Node-RED on IRIV PiControl.

Overview​

IRIV PiControl with Node-RED serves as a platform for Industry 4.0 applications, combining edge computing, industrial I/O, and visual programming for rapid deployment.

Target applications: Edge computing, IoT gateways, industrial automation, robotics, data logging, energy management systems.

Sensor Monitoring and Data Acquisition​

Real-Time Monitoring​

Pattern: Continuous sensor reading and visualization

Components:

  • Analog inputs (AI0-AI3) for voltage/current sensors
  • Digital inputs (DI0-DI3) for discrete sensors
  • Dashboard gauges and charts for visualization
  • Threshold monitoring for alarms

Typical sensors:

  • Temperature sensors (0-10V or 4-20mA)
  • Pressure sensors
  • Flow meters
  • Proximity sensors (digital)
  • Door/limit switches (digital)

Flow structure:

Periodic Inject → Read ADC → Process Data → Dashboard Display
                            → Check Thresholds → Trigger Alarm

Multi-Point Data Collection​

Pattern: Aggregating data from multiple sensors

Use cases:

  • Environmental monitoring (temperature, humidity, air quality)
  • Machine condition monitoring (vibration, temperature, pressure)
  • Energy monitoring (voltage, current, power factor)

Data handling:

  • Real-time dashboard display
  • Local database logging
  • Cloud data transmission
  • CSV file export

Process Control and Automation​

Sequential Control​

Pattern: Step-by-step process automation

Applications:

  • Manufacturing assembly sequences
  • Material handling automation
  • Batch processing control

Control elements:

  • Digital outputs (DO0-DO3) for actuator control
  • Digital inputs for feedback and interlock signals
  • Timer functions for dwell periods
  • State machine logic in function nodes

Example sequence:

Start → Activate Output 1 → Wait for Input 1 → Activate Output 2 → Complete

Conditional Logic Control​

Pattern: Decision-based automation

Applications:

  • Quality control sorting
  • Safety interlock systems
  • Adaptive process control

Logic elements:

  • Switch nodes for conditional routing
  • Function nodes for complex decision logic
  • Multiple input/output coordination

Protocol Gateway and Conversion​

Serial Protocol Gateway​

Pattern: Bridge between different communication protocols

Use cases:

  • Modbus RTU to MQTT gateway
  • RS485 sensor network to cloud connector
  • Legacy equipment integration

Interfaces:

  • RS485 (/dev/ttyACM0) for Modbus devices
  • RS232 (/dev/ttyACM1) for legacy equipment
  • Ethernet (eth0, eth1) for network protocols
  • WiFi for cloud connectivity

Data flow:

RS485 Modbus Devices → Node-RED Processing → MQTT/Cloud

Multi-Protocol Integration​

Pattern: Unified data collection from diverse sources

Integration points:

  • Industrial sensors (analog, digital)
  • Modbus RTU devices (RS485)
  • Ethernet-connected PLCs
  • Cloud APIs and databases

Data Logging and Reporting​

Local Data Logging​

Pattern: Store sensor data for analysis and compliance

Storage options:

  • Local file system (CSV, JSON)
  • SQLite database
  • InfluxDB time-series database

Logged data:

  • Timestamped sensor readings
  • Process events and alarms
  • System status and diagnostics

Retention:

  • Configurable logging intervals
  • Automatic file rotation
  • Storage management

Remote Reporting​

Pattern: Transmit data to external systems

Destinations:

  • Cloud platforms (AWS IoT, Azure IoT, Google Cloud)
  • MQTT brokers
  • HTTP REST APIs
  • Email notifications

Use cases:

  • Remote monitoring dashboards
  • Alert notifications
  • Compliance reporting
  • Predictive maintenance data

Edge Computing Applications​

Local Decision Making​

Pattern: Process data and make decisions at the edge

Advantages:

  • Reduced latency
  • Continued operation during network outages
  • Reduced cloud data transmission costs

Functions:

  • Real-time threshold detection
  • Alarm generation
  • Emergency shutdown logic
  • Data filtering and aggregation

Predictive Maintenance​

Pattern: Monitor equipment condition for maintenance scheduling

Monitored parameters:

  • Vibration levels
  • Temperature trends
  • Operating hours
  • Cycle counts

Actions:

  • Alert when thresholds exceeded
  • Schedule maintenance notifications
  • Log historical trends

Energy Management​

Power Monitoring​

Pattern: Track energy consumption and efficiency

Measurements:

  • Voltage and current (analog inputs)
  • Power calculation (function nodes)
  • Energy accumulation over time

Outputs:

  • Real-time power display
  • Daily/monthly energy reports
  • Cost calculation
  • Peak demand tracking

Load Control​

Pattern: Manage electrical loads for efficiency

Control strategies:

  • Load shedding during peak demand
  • Scheduled equipment operation
  • Power factor correction

Implementation:

  • Digital outputs control contactors/relays
  • Analog inputs monitor power parameters
  • Timer-based scheduling

Machine Integration and Control​

PLC Communication​

Pattern: Interface with programmable logic controllers

Protocols:

  • Modbus RTU (RS485)
  • Modbus TCP (Ethernet)

Data exchange:

  • Read sensor data from PLC
  • Write setpoints to PLC
  • Monitor alarm states
  • Coordinate multiple PLCs

HMI Dashboard​

Pattern: Human-machine interface for operators

Dashboard elements:

  • Start/stop buttons
  • Status indicators
  • Process parameter displays
  • Alarm panels
  • Historical trend charts

Accessibility:

  • Web browser access (desktop, tablet, mobile)
  • Local network or remote access
  • Multi-user support

Robotics and Motion Control​

Coordinate Multiple Outputs​

Pattern: Synchronized control of multiple actuators

Applications:

  • Multi-axis positioning
  • Conveyor coordination
  • Gripper control sequences

Coordination:

  • Timed output sequences
  • Input-triggered actions
  • Feedback-based control

Industry 4.0 Integration​

IoT Gateway​

Pattern: Connect industrial equipment to IoT platforms

Functions:

  • Protocol translation
  • Data aggregation
  • Edge analytics
  • Bidirectional communication

Cloud platforms:

  • AWS IoT Core
  • Microsoft Azure IoT
  • Google Cloud IoT
  • ThingSpeak
  • MQTT brokers

Digital Twin Data Source​

Pattern: Provide real-time data for digital twin models

Data streams:

  • Sensor measurements
  • Equipment status
  • Process parameters
  • Environmental conditions

Implementation Resources​

Hands-On Tutorial: The official 13-part Node-RED tutorial series provides step-by-step implementation guidance for industrial applications.

Community Flows: Node-RED flow library contains shared examples for industrial automation patterns.

Source(s):

  • IRIV PiControl product page (my.cytron.io)
  • IRIV PiControl CM4 User Manual, Rev 1.3, Nov 2025
  • IRIV PiControl Node-RED Tutorial (my.cytron.io)