This subcomponent, part of the Self-awareness component, focuses on prognostics by predicting the Remaining Useful Life (RUL) for jet engines using sensor data. It uses a deep learning model combining Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) layers for RUL estimation.
The model is trained on the CMAPSS Jet Engine Simulated Data provided by NASA.
- Clone the repository:
git clone https://github.com/Modapto/Self-Awareness.git
cd Prognostics- Build the Docker image:
docker build -t prognostics_modapto .- Deploy the container:
docker run -dp 127.0.0.1:8568:8568 prognostics_modaptoRequests must include field "engine_index" as querystring argument. See the following examples. In these examples, it is assumed that you are testing from where you have deployed the image, hence the address is "localhost". If the container is in a remote machine, replace "localhost" with the IP address of that machine.
- Via CLI:
curl --request GET \
--url 'http://localhost:8568/analysis?engine_index=12'- Via Python script:
import requests
url = "http://localhost:8568/analysis"
querystring = {"engine_index":"12"}
payload = ""
response = requests.request("GET", url, data=payload, params=querystring)
print(response.text)This script loads the pre-trained model and prompts the user to enter a specific engine index. If no input is provided, it defaults to engine index 57 (press Enter to use the default).
- Response:
The response from the requests will look like this:
{
"RUL_prediction": "113.60893",
"UNIX_timestamp": "1724763078",
"engine_index": "12"
}--
Ilias Abdouni, Université de Lorraine
This project is licensed under the MIT License - see the LICENSE file for details.
A. Saxena, K. Goebel, D. Simon, and N. Eklund, ‘Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation’, in the Proceedings of the 1st International Conference on Prognostics and Health Management (PHM08), Denver CO, Oct 2008.