AI & Automation Case Study

PredictX Factory Machine AI

Industrial Machine Learning sensor model predicting motor breakdown 72 hours before failure occurs.

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Project Specifications

Client

PredictX Industrial AI

Industry

Industrial Manufacturing

Duration

9 Weeks

Completed

June 2024

Tech Stack
Python Scikit-Learn TensorFlow MQTT InfluxDB
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The Challenge

Unplanned assembly line motor failures cost PredictX client factories $50,000 per hour in idle time.

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Our Solution

WebZap trained an anomaly detection ML model analyzing vibration and thermal telemetry from IoT sensors.

Key Features Implemented

Specific technical features built into this application.

01

72-Hour Failure Warning

Predicts bearing wear and motor overheating 3 days before breakdown.

02

Vibration Frequency Analysis

FFT signal analysis detecting micro-fractures in rotating shafts.

03

Automated Work-Order Generation

Dispatches maintenance tickets to plant engineers with replacement part specs.

04

Live Plant Floor Map

Color-coded visual map showing healthy (green) and warning (red) machines.

Key Performance Indicators

Quantifiable results achieved post-launch.

-70%
Unplanned Machine Downtime
$450K
Annual Repair Costs Saved
98.6%
Failure Prediction Accuracy
“

PredictX AI model saved our assembly line from two major motor breakdowns this past quarter alone.

Venkat Subramanian
Plant Manager, PredictX
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