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Conundrum Aircraft Engine Demo

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Logo for Conundrum Aircraft Engine Demo

Description

Helm chart for Conundrum Deep Learning based Predictive Maintenance Demo using NASA-Turbofan Engine Degradation Simulation dataset

Publisher

Conundrum.AI

Latest Version

latest

Compressed Size

1.99 KB

Modified

September 24, 2020

Conundrum is an ISV partner for NVIDIA in the Industrial AI Predictive Maintenance domain. This Helm chart corresponds to the aircraft engine degradation simulation demo container using Conundrum's platform that in turn uses PyTorch and TensorFlow. The demo involves real-time data simulation of randomly picked aircraft from NASA's FD001 dataset, Inference model for failure probability and a dashboard with widgets displaying engine malfunction warnings, sensor influence, model quality and GPU usage.

Data Set Used in this Container

Turbofan Engine Degradation Simulation Data Set

Citation: A. Saxena and K. Goebel (2008). "Turbofan Engine Degradation Simulation Data Set", NASA Ames Prognostics Data Repository(https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/ case #6), NASA Ames Research Center, Moffett Field, CA

Description: Engine degradation simulation was carried out using C-MAPSS. Four different were sets simulated under different combinations of operational conditions and fault modes. Records several sensor channels to characterize fault evolution. The data set was provided by the Prognostics CoE at NASA Ames.

Procedure to fetch and run the Container

On a system installed with microk8s and Helm, do the following:

> sudo microk8s.helm fetch https://helm.ngc.nvidia.com/partners/charts/helm-conundrum-aircraftengine-cuda10-pytorch11-latest.tgz

> sudo microk8s.helm install --name test helm-conundrum-aircraftengine-cuda10-pytorch11-latest.tgz

> sudo microk8s.kubectl get pods

On the Chrome browser to monitor the demo run:

http://host-ip:32663 where host-ip is the ip address of the system with microk8s and Helm.