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Enhancing Efficiency, Reducing Defects with Predictive Maintenance for a Global Mobile Leader

Enhancing Efficiency, Reducing Defects with Predictive Maintenance for a Global Mobile Leader

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    Synopsis

    Technology Stack

    Azure Cosmos DB

    Selenium

    NodeJS

    AngularJS

    Python

    LSTM

    RNN

    Client Overview

    For one of the World’s Largest Telecom OEM.

    Challenge

    The Pick & Place Machines operate at an efficiency with 8-9 defects per million. So, the client wanted a solution which reduce the rate of defect in these machines with proper research & analysis.

    Solution

    Solution aims to reduce the defect rate to ~3.4 defects per million:

    • Data collected from sensors and video cameras captured on a cloud server and database maintained in a data lake
    • Placement time, Operating parameters, Panel information, error and event data captured at a 15 minute interval
    • Data lake for storing data from multiple sources –(sensors, software crawlers, raw files)
    • Machine learning model developed for identifying error rate and occurrence

    Outcome

    Low error rates
    Accuracy in data
    ~15% cost reduction

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