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 degrade, the sensor readings deviate from the learned normal patterns. The autoencoder reconstruction error increases, signaling anomaly.

Detection Window

In laboratory validation on 8 public datasets, the system detected degradation 5-24 days before actual failure. This window allows maintenance teams to schedule interventions 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% downtime reduction in digital twin simulation
• 6x 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 reconstruction error in real-time
4. A response protocol for when anomalies are detected

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