Case Study — Nexora
Predicting Equipment Failures 72 Hours in Advance Across a 500-Asset Fleet
$3.2M in annual maintenance savings through machine learning failure prediction
Industry
Capital Equipment Manufacturing
Timeline
16 weeks
Team
6 engineers
Tech
IoT + Python ML + TimescaleDB
The Challenge
A capital equipment manufacturer was experiencing $4.8M in annual unplanned downtime across a 500-asset fleet. Maintenance was entirely reactive — technicians responded to failures after they occurred. Parts inventory was bloated by 40% with safety stock for every possible failure mode, and MTTR averaged 18 hours due to unavailable parts.
Our Approach
How We Solved It
Vibration & Thermal Sensor Integration
Deployed IoT sensors measuring vibration signatures, temperature, current draw, and operating cycle counts on all 500 assets, creating a continuous health monitoring stream.
Failure Mode ML Models
Trained separate ML models for the 8 most common failure modes using 24 months of historical sensor data and maintenance records. Each model outputs a probability and confidence interval for failure within 72 hours.
Maintenance Work Order Automation
High-probability failure predictions automatically generate prioritized work orders in the CMMS with the predicted failure mode, required parts list, and technician skill requirement.
Parts Inventory Optimization
Used prediction data to right-size parts inventory — stocking based on predicted demand rather than safety stock rules, reducing inventory carrying cost by $1.8M annually.
Engineering Process
How We Built It
Feature Engineering From Sensor Data
Raw sensor readings are transformed into engineered features (rolling statistics, frequency domain features, operating regime classification) that capture the health degradation signal rather than raw values.
Concept Drift Detection
Each model monitors its own prediction accuracy and triggers retraining when accuracy degrades beyond a threshold — keeping models accurate as equipment ages and operating conditions change.
Uncertainty Quantification
Models output confidence intervals alongside predictions so maintenance planners can make risk-adjusted decisions: dispatch immediately for high-confidence predictions, schedule next week for low-confidence ones.
Architecture Decisions
Key Technical Choices
Edge Inference for Latency-Critical Assets
For the 40 highest-criticality assets, failure detection runs on-device using ONNX-optimized models — triggering immediate alerts without cloud round-trips that could delay critical interventions.
Separate Models per Failure Mode
Training one model per failure mode rather than a multi-class classifier significantly improved accuracy per failure mode and made it easy to update individual models as new failure data arrives.
CMMS Integration as the Delivery Mechanism
Delivering predictions through the existing CMMS (rather than a new app) meant zero behavior change for maintenance technicians — adoption was immediate because the workflow was familiar.
Results
What We Delivered
Solution Blueprint
How It All Fits Together
- 500 IoT sensor nodes
- Edge inference (40 critical assets)
- Real-time telemetry stream
- 8 failure mode ML models
- Concept drift detection
- Uncertainty quantification
- CMMS work order integration
- Parts demand forecasting
- Technician dispatch dashboard
Lessons Learned
What We Improved
Start With the 10 Highest-Cost Failures
We prioritized the 10 failure modes responsible for 78% of downtime cost rather than trying to predict everything. Getting those 10 right funded the next phase.
Model Accuracy Requires Maintenance History Quality
The first 6 weeks were spent cleaning 24 months of CMMS maintenance records. Poor failure labeling in historical data produces models that predict everything or nothing.
False Positives Kill Trust Faster Than False Negatives
Two false positive work orders in week 3 nearly ended the program. We adjusted the alert threshold to prioritize precision over recall until trust was established.
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