EquipSense

Predictive Maintenance: $1.5M Savings Per Plant

The Problem

Unplanned equipment failures were catastrophic. Each failure caused 8-12 hour production stops averaging $150K in lost production. Reactive maintenance cost $8M/year across 200 plants. Plants had no visibility into which machines would fail next.

The Solution

Kurai deployed a predictive maintenance system using LSTM neural networks trained on 3 years of sensor data (50K sensors × 3 years = 450M data points). The system:

• Analyzes vibration, temperature, pressure, acoustic signatures
• Predicts failures 7-14 days in advance
• Prioritizes maintenance by failure probability and cost impact
• Integrates with SCADA systems and maintenance work orders

Model Architecture:

• Algorithm: LSTM (Long Short-Term Memory) networks
• Features: 27 sensor readings + equipment metadata
• Training: 3 years of data, 80/10/10 train/val/test split
• Deployment: Edge ML (TensorFlow Lite) on IoT gateways

The Results

• Unplanned downtime: 8 hr/month → 1.2 hr/month (85% reduction)
• Maintenance cost: $8M/year → $2.5M/year (69% reduction)
• Equipment lifespan: +27% through proactive care
• ROI: $1.5M savings per plant annually

Technology Stack

TensorFlow 2.13, Keras, TensorFlow Lite (edge), InfluxDB (time-series), Kafka, Grafana, REST API to SCADA

Key Metrics

Metric	Before	After	Improvement
Unplanned downtime	8 hr/month	1.2 hr/month	-85%
Maintenance cost	$8M/year	$2.5M/year	-69%
Prediction accuracy	N/A	92%	New
Equipment lifespan	8 years	10.2 years	+27%

EquipSense prevents $1.5M in losses per plant annually by predicting failures 7-14 days in advance.