Predictive Analysis and Fault Diagnostics of Jet Engine using Google Cloud Platform
Executive Summary
The client is an EU-based multinational aerospace leader supplying aircraft engines, rocket engines, aerospace components, and defense equipment. It is also a key provider of inertial navigation systems (INS) used in air, land, and naval applications. In addition, the client is a leader in helicopter flight controls, optronics, and tactical UAV systems.
In the aviation industry, fuel quality data plays a crucial role in enhancing safety, managing operational costs, and achieving peak performance. The client wanted to collect fuel data and upload it to the cloud on where the data analysis could be performed. The eInfochips team designed and implemented an algorithm using machine learning, predictive analysis, BigQuery, cloud flow, and deep neural networks. The setup consists of complex nonlinear simulation of a turbofan jet engine along with a thrust control system.
Project Highlights
- Throttle fault detection
- Predictive Analysis of faults for jet engine sensor data
- Real-time monitoring of fuel-data
- Stream analytics
- IoT core for Android Things
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Executive Summary
The client is an EU-based multinational aerospace leader supplying aircraft engines, rocket engines, aerospace components, and defense equipment. It is also a key provider of inertial navigation systems (INS) used in air, land, and naval applications. In addition, the client is a leader in helicopter flight controls, optronics, and tactical UAV systems.
In the aviation industry, fuel quality data plays a crucial role in enhancing safety, managing operational costs, and achieving peak performance. The client wanted to collect fuel data and upload it to the cloud on where the data analysis could be performed. The eInfochips team designed and implemented an algorithm using machine learning, predictive analysis, BigQuery, cloud flow, and deep neural networks. The setup consists of complex nonlinear simulation of a turbofan jet engine along with a thrust control system.
Project Highlights
- Throttle fault detection
- Predictive Analysis of faults for jet engine sensor data
- Real-time monitoring of fuel-data
- Stream analytics
- IoT core for Android Things
To download this resource
Fill in the details below
Executive Summary
The client is an EU-based multinational aerospace leader supplying aircraft engines, rocket engines, aerospace components, and defense equipment. It is also a key provider of inertial navigation systems (INS) used in air, land, and naval applications. In addition, the client is a leader in helicopter flight controls, optronics, and tactical UAV systems.
In the aviation industry, fuel quality data plays a crucial role in enhancing safety, managing operational costs, and achieving peak performance. The client wanted to collect fuel data and upload it to the cloud on where the data analysis could be performed. The eInfochips team designed and implemented an algorithm using machine learning, predictive analysis, BigQuery, cloud flow, and deep neural networks. The setup consists of complex nonlinear simulation of a turbofan jet engine along with a thrust control system.
Project Highlights
- Throttle fault detection
- Predictive Analysis of faults for jet engine sensor data
- Real-time monitoring of fuel-data
- Stream analytics
- IoT core for Android Things
To download this resource
Fill in the details below