Blazor DataGrid Example - AI-Powered Anomaly Detection
This sample demonstrates how the syncfusion DataGrid, enhanced with AI, can detect anomalies within its data
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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. |
In this example, the DataGrid displays details like 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.