How do I detect equipment failure before it happens?

How It Works

An LSTM autoencoder is trained on healthy equipment sensor data. It learns the normal patterns of vibration, temperature, pressure, and other signals. When the equipment starts to wear down, the sensor readings change from the learned normal patterns. The system's error rate increases, signaling a problem.

Detection Window

In laboratory validation on 8 public datasets, the system detected wear 5-24 days before actual failure. This window allows maintenance teams to plan repairs instead of responding to emergencies.

Validation Results

• AUC up to 1.000 on individual datasets
• AUC 0.9995 on cross-dataset transfer (trained on one equipment type, tested on another)
• Zero false alarms on transfer test
• 72.9% downtime reduction in digital twin simulation
• 4.87x lower maintenance costs in simulation

What You Need

1. Sensor data from critical rotating equipment (vibration, temperature, pressure)
2. Historical data of healthy operation (enough to train the model)
3. A system to monitor the error rate in real-time
4. A plan for when problems are detected

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