Predictive Maintenance using Machine learning on GCP

Understanding Predictive Maintenance Applications


Predictive maintenance covers diverse application areas, such as –


  • Manufacturing industry
  • Information and technology
  • Aerospace
  • Heavy-Machinery sector
  • Predicting future performance of a subsystem or a component to make RUL (Remaining Useful Life) estimation.


Business Challenge for Enabling Predictive Maintenance


  • Monitoring of Assets in Real-Time via sensor data patterns to predict the breakdown of Assets.
  • Production systems deteriorate with the time and need maintenance.
  • The regular way to keep the system good is to apply preventive maintenance practices, in the case of clearly detected malfunctions or equipment breakdowns. All this affects the quality, cost and in general, productivity.


Other than this, the uncertainty of machine reliability at any given time also impacts on product/production delivery times.


Predictive Maintenance Analytics Pipeline


Collecting targeted data


The targeted data reside in remote locations and gets into the analysis pipeline including sensors, meters, supervisory control, etc. Collect data from all of the remote data sources to learn and continually make better, more informed business decisions.


Determining Analytics Pipeline


Establish an Advanced Analytics Pipeline based on the specific operation. Cloud analytics should be balanced to reduce the burden of streaming perishable PdM data on Cloud Deployment. Follow a distributed approach to detect and respond to local events at Cloud dataflow consumer step, take immediate action on Streaming data, while simultaneously integrating additional data sources in the Cloud.


Technology Stack –

  • Python
  • Flask
  • Cloud IoT Core
  • Cloud Pub/Sub
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