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.