Industrial PLC Automation System

Welcome to the Industrial PLC Automation project! This repository contains a microservices-based architecture designed to simulate, monitor, and control an automated manufacturing plant using modern web technologies and Kubernetes.

The system is integrated with the KubeMind AI Platform, an autonomous site reliability engineering (SRE) engine that uses AI to monitor cluster health, detect anomalies, predict failures, and suggest autonomous remediation strategies.

System Architecture

The architecture is built upon an API Gateway pattern that routes requests to individual industrial microservices. The state and metrics of these microservices are actively monitored by the KubeMind AI metrics engine.

graph TD
    %% Define styles
    classDef frontend fill:#3b82f6,stroke:#1e3a8a,stroke-width:2px,color:#fff;
    classDef gateway fill:#8b5cf6,stroke:#4c1d95,stroke-width:2px,color:#fff;
    classDef service fill:#10b981,stroke:#064e3b,stroke-width:2px,color:#fff;
    classDef ai fill:#f59e0b,stroke:#78350f,stroke-width:2px,color:#fff;

    %% Client and Dashboards
    Client["Client / Operator"]
    PLCDash["PLC Dashboard (Next.js)"]:::frontend
    AIDash["AI Dashboard (Next.js)"]:::frontend

    %% Services
    APIGW("API Gateway"):::gateway
    PLC[PLC Controller]:::service
    Sensor[Sensor Service]:::service
    Motor[Motor Service]:::service
    Conveyor[Conveyor Service]:::service
    Robot[Robot Arm Service]:::service
    Alarm[Alarm Service]:::service
    Energy[Energy Service]:::service
    Notify[Notification Service]:::service

    %% AI Engine
    KubeMind((KubeMind AI Engine)):::ai

    %% Connections
    Client -->|HTTP / WS| PLCDash
    Client -->|HTTP / WS| AIDash
    Client -->|HTTP| APIGW

    APIGW -->|/api/plc| PLC
    APIGW -->|/api/sensor| Sensor
    APIGW -->|/api/motor| Motor
    APIGW -->|/api/conveyor| Conveyor
    APIGW -->|/api/robot| Robot
    APIGW -->|/api/alarm| Alarm
    APIGW -->|/api/energy| Energy
    APIGW -->|/api/notification| Notify

    %% Internal Data Flow
    PLC --> Motor
    PLC --> Conveyor
    PLC --> Robot
    Sensor --> PLC
    Sensor --> Alarm
    Sensor --> Energy
    Alarm --> Notify

    %% KubeMind Monitoring
    KubeMind -.->|Monitors Metrics| APIGW
    KubeMind -.->|Monitors Metrics| PLC
    KubeMind -.->|Provides AI Insights| AIDash

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Key Components

1. API Gateway (api-gateway): Centralized entry point on port 4000. Handles routing and WebSocket connections.
2. PLC Controller (plc-controller): The "brain" of the factory line, dispatching commands to actuators and reading from sensors.
3. Actuator Services:
   • motor-service: Controls industrial motors.
   • conveyor-service: Manages conveyor belt speeds and states.
   • robot-arm-service: Coordinates robotic arm kinematics.
4. Monitoring & Alerting:
   • sensor-service: Generates mock telemetry data (temperature, pressure).
   • energy-service: Tracks power consumption.
   • alarm-service: Evaluates threshold breaches.
   • notification-service: Dispatches alerts.
5. Dashboards:
   • PLC Dashboard: Real-time factory floor UI.
   • AI Dashboard: Real-time cluster topology, anomaly detection, and AI SRE recommendations.

How to Run Locally

You can launch the entire stack using the provided startup script. This will boot all microservices, Next.js dashboards, and the Python FastAPI metrics engine concurrently.

# Make the script executable
chmod +x ./start_services.sh

# Run the stack
./start_services.sh

Note for KubeMind AI Features: To enable real-time metrics gathering for the AI dashboard, you must have your local Kubernetes cluster (e.g., minikube) running and kube-prometheus-stack installed.

Kubernetes Deployment

To deploy the services to your Kubernetes cluster:

# Build Docker images for all services
./scripts/build-plc-images.sh

# Apply all manifests to the cluster
./scripts/deploy-plc.sh

Stopping the Services

If you ran the services locally using ./start_services.sh, simply press Ctrl+C in the terminal running the script. The script's trap will automatically capture the signal and gracefully terminate all background processes.