Predictive Maintenance with IoT Sensors: TensorFlow LSTM and InfluxDB

Unplanned machinery downtime costs manufacturers $50B annually. Predictive maintenance uses sensor data to forecast failures before they occur.

1. Data Pipeline

IoT sensors stream vibration, temperature, and pressure readings via MQTT to a Node.js ingestion server. Data is stored in InfluxDB for time-series analysis:

typescript
await influxDB.writeApi.writePoint(
  new Point('sensor_reading')
    .tag('machine_id', machineId)
    .floatField('vibration', reading.vibration)
    .floatField('temperature', reading.temperature)
    .timestamp(new Date())
);

2. LSTM Prediction Model

We train an LSTM network on historical failure sequences to predict Remaining Useful Life (RUL) of machinery components.

3. Real-Time Dashboard

WebSocket connections push live sensor data and prediction scores to a React dashboard, alerting engineers when RUL drops below safety thresholds.

Summary

Combining time-series databases with deep learning creates an early warning system that prevents catastrophic failures and reduces maintenance costs by 25%.