Optimizing industrial data for real-time monitoring and insightful dashboards.
In the era of Industry 4.0, the ability to monitor equipment in real-time is crucial. However, raw data from CNC machines is often unstructured and difficult to interpret. To create an effective Data Visualization, we must implement a robust method for structuring machine status data.
Why Data Structuring Matters
Without a clear schema, latency increases and visual tools like Grafana or custom web dashboards fail to render information accurately. By organizing data into logical objects—such as timestamps, operational modes, and error codes—we ensure seamless integration.
The Proposed Data Schema (JSON Example)
A standard approach is to use a JSON-based structure. This format is lightweight and highly compatible with modern web technologies.
{
"machine_id": "CNC-AXIS-01",
"timestamp": "2024-05-20T10:30:00Z",
"status": {
"state": "Running",
"mode": "Auto",
"spindle_speed": 12000,
"load_percentage": 75
},
"alerts": [
{"code": "W02", "message": "Coolant Low"}
]
}
Visualizing the Machine Status
Once the data is structured, we can map it to visual components. For instance, the state variable can trigger color changes in a UI (Green for Running, Red for E-Stop). Structured CNC machine status data allows engineers to perform predictive maintenance and reduce downtime significantly.
Key Metrics to Track:
- Cycle Time: Duration of the machining process.
- OEE (Overall Equipment Effectiveness): To measure productivity.
- Axis Position: For real-time spatial monitoring.
