Predictive Maintenance using Machine learning on GCP

Understanding Predictive Maintenance Applications

Predictive maintenance nowadays gaining popularity among enterprises that predicts failure of the system, and the actions could include corrective actions, the replacement of the system, or even planned failure. This helps enterprises to cost savings, greater predictability, and the improved availability of the systems. Predictive maintenance sidesteps both the limits and maximizes the use of its sources. Predictive maintenance also detects the irregularities and failure patterns and provide real-time alerts. These signals can facilitate efficient maintenance of those components. AI-enabled Predictive Maintenance is uncommon in that instead of just predicting impending failure, it also attempts to provide outcome-focused instructions for operations and maintenance from analytics. Let’s explore the areas where predictive maintenance can be used:

 

Predictive maintenance covers diverse application areas, such as –

 

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

In this use case, we will guide you through how to build a machine learning platform for predictive maintenance.

 

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 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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