Predictive maintenance Industry 4.0

Predictive maintenance Industry 4.0

Customer Challenges:

  • A Gas Pump can fail anytime without any warning, causing significant loss of production
  • Ability to predict any possibility of failure is critical


  • Real time monitoring and predictive failure analysis is expected to save 1500 hours of downtime resulting in savings of $100K per month.
  • Prescriptive recommendation for informed decision
  • 10 % reduction in downtime

CoreView Solution:

  • Analysis of existing dataset which consisted of 5000 pump’s historical operating data for the past 2 years.
  • Building a data model using Python/Spark/Cassandra pipeline
  • Advanced mathematical models based on time series forecasting and classifiers
  • Visualization of the results using D3 libraries

Other Considerations:

  • Silos data and Scale of the data – pumps, logs, environmental, SOPs etc
  • Parameters for pump failures are not well defined

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