Blazor DataGrid Example - AI-Powered Anomaly Detection
Machine ID | Temperature (C) | Pressure (psi) | Voltage (V) | Motor Speed (rpm) | Production Rate (units/hr) | Anomaly Description |
|---|
| M001 | 85 | 120 | 220 | 1500 | 100 | The factors that supporting the Production rate is relevant to the count produced, hence the row data is marked as normal data. |
| M002 | 788 | 115 | 230 | 1520 | 105 | The factors that supporting the Production rate is relevant to the count produced, hence the row data is marked as normal data. |
| M003 | 90 | 118 | 225 | 1480 | 95 | The factors that supporting the Production rate is relevant to the count produced, hence the row data is marked as normal data. |
| M004 | 87 | 122 | 228 | 1515 | 110 | The factors that supporting the Production rate is relevant to the count produced, hence the row data is marked as normal data. |
| M005 | 92 | 116 | 222 | 21475 | 980 | The factors that supporting the Production rate is relevant to the count produced, hence the row data is marked as normal data. |
| M006 | 85 | 119 | 220 | 1490 | 102 | The factors that supporting the Production rate is relevant to the count produced, hence the row data is marked as normal data. |
| M007 | 88 | 114 | 230 | 1500 | 104 | The factors that supporting the Production rate is relevant to the count produced, hence the row data is marked as normal data. |
| M008 | 90 | 1120 | 225 | 1470 | 89 | The factors that supporting the Production rate is relevant to the count produced, hence the row data is marked as normal data. |
| M009 | 87 | 121 | 228 | 1505 | 108 | The factors that supporting the Production rate is relevant to the count produced, hence the row data is marked as normal data. |
| M010 | 92 | 117 | 222 | 1480 | 100 | The factors that supporting the Production rate is relevant to the count produced, hence the row data is marked as normal data. |
This sample demonstrates how the Syncfusion DataGrid, enhanced with AI, can detect anomalies within its data.
Explore our Smart AI demos with limited AI token usage directly in your browser. To dive deeper and experiment with these features locally using your own API key, check out our Syncfusion Smart AI Samples on GitHub.
In this example, the DataGrid displays details such as Machine ID, Voltage, Pressure, Temperature, Motor Speed, and Production Rate. AI analyzes this data to identify unusual points and explains why they are considered anomalies. When you press the "Detect Anomaly" button, the grid updates to display the anomaly details.