Predictive Maintenance System

Overview

A manufacturing operation running 24/7 with 50+ critical machines was hemorrhaging money from unexpected breakdowns. Each unplanned stop cost $15K-$50K in lost production, emergency parts, and overtime labor. Their maintenance strategy was purely reactive — fix it when it breaks. We deployed an AI predictive maintenance system across all critical equipment that monitors vibration, temperature, pressure, and acoustic signatures to predict failures weeks before they occur.

The Challenge

Industrial equipment fails in complex ways — bearing wear, lubrication degradation, electrical faults, and structural fatigue all have different signatures. The AI needed to learn normal operating patterns for each machine (which vary by product being made, ambient conditions, and machine age), detect anomalies that indicate developing problems, and distinguish between normal variation and genuine warning signs. False alarms are expensive too — unnecessary maintenance stops cost production time.

Our Approach

We installed IoT sensors on critical equipment and built machine-specific models that learn normal operating profiles. Anomaly detection runs continuously, comparing real-time readings against expected ranges. When deviation patterns match known failure signatures, the system generates a maintenance recommendation with confidence level, estimated time-to-failure, required parts, and suggested maintenance window. The maintenance team gets a prioritized dashboard that integrates with their CMMS for work order generation.

Key Features

• Real-time monitoring of all critical equipment
• Failure prediction 2-4 weeks in advance
• Machine-specific baseline learning
• Root cause classification for predicted failures
• Parts forecasting and pre-ordering
• CMMS integration for automatic work orders
• Maintenance ROI tracking and reporting

Results

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