Predictive Maintenance System

Detect Machine Faults
Before They Happen

Upload a CSV exported from your PLC, select the sensor columns, and let the AI diagnose your packaging machine in seconds.

98.96%Model Accuracy

6Fault Types

22Sensor Inputs

<1sAnalysis Time

Upload PLC Data Step 1 of 2

📂

Drop your CSV file here

Exported from TIA Portal, RSLogix, or Unity Pro

Select Sensor Columns Step 2 of 2

— file loaded —

Select exactly 22 sensor columns to feed into the model. Exclude Timestamp, Status, and label columns.

0 / 22 selected

Running neural network inference...

Diagnostic Report —

Fault Distribution

Sample Predictions (first 10 rows)

#	T_conv	I_motor	Vib_x	Diagnosis	Confidence	Action

How It Works

01 —

Export from PLC

Export sensor readings as CSV from TIA Portal, RSLogix, or any SCADA system.

02 —

Select Columns

Choose the 22 sensor columns relevant to your machine. Exclude timestamps and labels.

03 —

AI Inference

A neural network (MLP) trained on packaging machine data classifies each row.

04 —

Take Action

Review the fault distribution report and schedule maintenance before failure occurs.

Detect Machine FaultsBefore They Happen