Predictive Analytics Using Google Cloud Platform Based Deep Learning

Executive Summary

Predictive analytics and fault diagnostics of high-value engineering systems often involve processing of real-time sensor data to deduct impeding or hidden problems within the underlying system. To achieve this, it is necessary to condition the data from sensors and thereafter combine it with prior design knowledge of the system in order to create useful insights. The benefits of such analytics include abilities to prevent costly failures and cost-effective scheduling of maintenance cycles. To implement such a system, it is necessary to create a setup that can effectively enable data capture, data conditioning, secure transmission, database storage and execute real-time data analytics algorithms. Most of the cloud-enabled IoT platforms are evolving in this direction while addressing different domains such as engineering systems, retail data processing, and surveillance applications. Google cloud platform efficiently provides components that can enable an effective and quick implementation of data-driven analytics systems.

This white paper presents an aircraft jet engine sensor fault diagnostics and prediction implementation using Google cloud platform. The use cases covered are effectively implemented to demonstrate the GCP capability to implement such a prognostic system.

Project Highlights

Aircraft Jet Engine Failure Analytics Using Google Cloud Platform Based Deep Learning
  • Google IoT Cloud At-a-Glance
  • Overview of Google IoT System
  • Android Things for GCP IoT System
  • Google Cloud Platform and IOT Components
  • Details of GCP Services
  • Google Cloud Platform as an end-to-end IoT and Analytics Solution
  • Secure provisioning, logging and over the air updates
  • Easy Setup and Managed Infrastructure
  • Powerful Analytics
  • Case Study: Jet Engine Data Collection and Data Analytics using the Google Cloud Platform
  • Data representation using BigQuery and Data Studio
  • Data Analysis
  • Predictive Analysis of Faults for Jet Engine Sensor Data
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