Industrial IoT Machine Monitoring — Predictive Maintenance for Manufacturing
A 200-sensor industrial IoT system for a textile plant ingesting 10,000+ data points/second with predictive maintenance AI — reducing unplanned downtime by 34% and achieving ROI in 4 months.
⚠The Problem
A textile plant with 80 production machines was experiencing unplanned downtime averaging $20,000/day. Bearing failures were only detected after catastrophic failure — by that time, the shaft was also damaged, multiplying repair costs. Machine utilization was tracked in a paper logbook. Energy consumption per machine was unknown, making it impossible to identify energy-intensive outliers.
💡Our Solution
We installed MEMS vibration sensors on all rotating machinery (motors, gearboxes, conveyor drives) connected via Modbus to ESP32 gateway nodes. Each node samples vibration at 1,600 Hz, performs local FFT analysis for dominant frequency components, and publishes both raw and processed data to AWS IoT Core via MQTT. InfluxDB ingests 10,000+ data points per second with automatic 30-day raw retention and 1-year downsampled retention. A Python ML service applies isolation forest anomaly detection on FFT signatures to detect bearing degradation 2–4 weeks before failure. Current clamps measure per-machine energy consumption, feeding a real-time OEE dashboard.
🔗System Architecture
Sensors (Modbus) → ESP32 Gateways → MQTT → AWS IoT Core → InfluxDB → Python ML → React OEE DashboardTech Stack
- ESP32 gateway nodes
- MEMS vibration sensors (ADXL345)
- Current clamps (SCT-013)
- Modbus RTU PLC interface
- Thermocouple + RTD temperature
- Industrial DIN-rail enclosures (IP54)
- Modbus RTU → ESP32 gateway
- MQTT over Wi-Fi / Ethernet
- RS485 for legacy PLCs
- AWS IoT Core
- InfluxDB (10K points/sec)
- Python ML service (vibration FFT analysis)
- SNS (alert routing)
- React OEE dashboard (WebSocket)
- Real-time vibration spectrum charts
- Predictive maintenance alert panel
Key Features
Results Delivered
- 34% reduction in unplanned downtime
- 200+ sensors ingesting 10,000 data points per second
- ROI achieved in under 4 months
- Bearing replacements now scheduled during planned maintenance windows
- Energy outlier machines identified — 12% energy savings from corrections
Technologies
Who This Is For
Manufacturing plants, textile mills, food processing facilities, pharmaceutical manufacturers
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Frequently Asked Questions
What technology does this industrial iot machine monitoring use?+
Industrial IoT Machine Monitoring — Predictive Maintenance for Manufacturing is built on ESP32, Modbus, MQTT, InfluxDB, React. The stack was chosen for real-world reliability in a production deployment — full details are in the tech stack section.
Can CodeCaracal build a similar system for us?+
Yes. The same team that designed and deployed this manufacturing / industry 4.0 system takes on new projects across the stack — firmware, connectivity, cloud and dashboards. Tell us your requirements and we will architect a solution for your environment.
Is this case study a real deployed system?+
Yes — every case study on this site describes a production system we designed and shipped. The problem, architecture, features and metrics are from the actual deployment.
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