Predictive Maintenance Platform

The Challenge

An industrial equipment manufacturer with 3 production facilities was losing significant production time to unplanned equipment failures. Their maintenance strategy was primarily reactive - fix it when it breaks - with some calendar-based preventive maintenance that often replaced parts too early or too late.

The impact was substantial:

• $12,000-45,000 per hour of unplanned downtime (depending on line)
• Average of 340 hours of unplanned downtime annually
• Excess spare parts inventory "just in case"
• Maintenance technician burnout from emergency repairs

"We knew the data from our equipment could tell us something, but we had thousands of sensors and no way to make sense of it all. By the time something showed up on a trend chart, it was usually too late."

— Maintenance Manager

The Solution

We deployed a predictive maintenance platform that continuously analyzes sensor telemetry to detect early warning signs of equipment degradation.

flowchart TB
    subgraph Equipment[Equipment Sensors]
        Vib[Vibration]
        Temp[Temperature]
        Current[Motor Current]
        Pressure[Pressure]
        Flow[Flow Rates]
    end
    
    subgraph Ingest[Data Ingestion]
        Edge[Edge Processing]
        Stream[Time-Series DB]
        History[Historical Data]
    end
    
    subgraph ML[Prediction Engine]
        Anomaly[Anomaly Detection]
        Degrade[Degradation Models]
        RUL[Remaining Life Estimation]
    end
    
    subgraph Output[Outputs]
        Alert[Maintenance Alerts]
        Schedule[Work Order Generation]
        Dashboard[Asset Health Dashboard]
        Parts[Parts Forecasting]
    end
    
    Vib --> Edge
    Temp --> Edge
    Current --> Edge
    Pressure --> Edge
    Flow --> Edge
    
    Edge --> Stream --> Anomaly
    History --> Degrade
    Anomaly --> Degrade --> RUL
    
    RUL --> Alert --> Schedule
    RUL --> Dashboard
    RUL --> Parts
                

Multi-Signal Anomaly Detection

Individual sensors rarely tell the full story. A slight vibration increase combined with a small temperature rise and minor current fluctuation might individually look normal but together indicate bearing wear. Our models analyze cross-signal correlations to catch early degradation.

Equipment-Specific Models

A CNC machine fails differently than a hydraulic press. We trained separate models for each equipment type, using historical failure data to learn the specific signatures of different failure modes:

• Bearing failures (vibration + temperature patterns)
• Pump degradation (pressure + flow + current)
• Drive system wear (current draw + vibration)
• Seal failures (pressure decay patterns)

Maintenance Integration

Predictions automatically generate work orders in their CMMS with:

• Predicted failure mode and likely root cause
• Recommended parts based on failure type
• Optimal maintenance window (balancing production schedule and failure risk)
• Historical repair procedures for similar issues

Results

After 12 months in production:

• 41% reduction in unplanned downtime
• $1.8M annual savings from avoided production losses
• 23% reduction in spare parts inventory
• 87% accuracy on failure predictions (2-3 week window)
• Maintenance team shifted from reactive to planned work

"Last month we predicted a spindle bearing failure 18 days before it would have happened. Replaced it over the weekend during planned downtime. That single catch saved us $180,000 in emergency repairs and lost production."

— Plant Manager

Technical Details