IoT Predictive Maintenance System — Water Pump Station Monitoring
An ESP32 vibration monitoring system deployed across 45 pump stations — using FFT and ML to predict bearing failures 3–4 weeks in advance, reducing emergency repair calls by 60% and slashing maintenance costs.
⚠The Problem
A water utility operating 45 pump stations was spending $1.8M/year on emergency pump repairs and emergency contractor call-outs. Pumps failed without warning, disrupting water supply to residential areas and triggering SLA breach penalties. Maintenance was calendar-based (every 6 months) regardless of actual condition — some pumps were overmaintained, others failed between maintenance cycles.
💡Our Solution
We installed ADXL355 high-resolution MEMS accelerometers on each pump's motor bearing housing, connected to ESP32 nodes. The ESP32 samples vibration at 3,200 Hz and performs on-device FFT, extracting 1×, 2×, 3×, and BPFO/BPFI bearing fault frequencies per ISO 10816. Data is published to InfluxDB every 5 minutes. A Python ML model (trained on 6 months of baseline data) calculates a bearing health score (0–100) per pump and projects failure probability for the next 30 days. When a pump reaches health score < 60, a work order is automatically created in ServiceNow CMMS with the predicted failure date and recommended action.
🔗System Architecture
ESP32 (FFT on-device) → MQTT → AWS IoT Core → InfluxDB → Python ML → React Dashboard + ServiceNow CMMSTech Stack
- ESP32 with MEMS accelerometer (ADXL355)
- Current transformer (motor current)
- Temperature (winding + bearing)
- Vibration sensor (piezoelectric)
- Industrial enclosures (ATEX-rated zones)
- Wi-Fi / Ethernet (pump station network)
- MQTT to AWS IoT Core
- 4G where no site network available
- AWS IoT Core
- InfluxDB (vibration + current history)
- Python ML service (ISO 10816 FFT analysis)
- ServiceNow CMMS integration (work orders)
- React pump health dashboard
- FFT spectrum viewer per pump
- Predicted failure timeline
- CMMS work order tracking
Key Features
Results Delivered
- 60% reduction in emergency repair call-outs
- Average failure prediction: 3–4 weeks advance notice
- 45 pump stations fully instrumented
- $1.1M maintenance cost reduction in first year
- Water supply interruptions reduced from 18 to 3 in 12 months
Technologies
Who This Is For
Water utilities, oil & gas operators, HVAC companies, industrial pump operators
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