Matlab turbofan project
Budget: $250 – $750 USD
Project 1: Remaining Useful Life Prediction - When does the next fault occur?
Prognostics Center of Excellence - Data Repository (nasa.gov)
https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/
Data Set 6:
Turbofan Engine Degradation Simulation Dataset, provided by NASA, is becoming an important benchmark in the Remaining Useful Life (RUL) estimation for a fleet of engines of the same type (100 in total). Data are available in the form of time series: 3 operational settings, 21 sensor measurements and cycle — i.e. observations in terms of time for working life.
Engines have different life durations. The average working time in train data is 206 cycles with a minimum of 128 and a maximum of 362.
The goal is to develop a predictive model, which can predict the remaining life cycles based on the current operational setting and sensor measurements.
Here is a possible approach developed by Marco Cerliani.
Remaining Life Estimation with Keras
https://towardsdatascience.com/remaining-life-estimation-with-keras-2334514f9c61
...
I did ANN to fine RUL but doctor said wrong , it should be like this https://towardsdatascience.com/remaining-life-estimation-with-keras-2334514f9c61 , which is CNN .. YOU CAN FIND DOCTOR COMMENTS IN ATTACHED AND REPORT AND MATLAB >
Prognostics Center of Excellence - Data Repository (nasa.gov)
https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/
Data Set 6:
Turbofan Engine Degradation Simulation Dataset, provided by NASA, is becoming an important benchmark in the Remaining Useful Life (RUL) estimation for a fleet of engines of the same type (100 in total). Data are available in the form of time series: 3 operational settings, 21 sensor measurements and cycle — i.e. observations in terms of time for working life.
Engines have different life durations. The average working time in train data is 206 cycles with a minimum of 128 and a maximum of 362.
The goal is to develop a predictive model, which can predict the remaining life cycles based on the current operational setting and sensor measurements.
Here is a possible approach developed by Marco Cerliani.
Remaining Life Estimation with Keras
https://towardsdatascience.com/remaining-life-estimation-with-keras-2334514f9c61
...
I did ANN to fine RUL but doctor said wrong , it should be like this https://towardsdatascience.com/remaining-life-estimation-with-keras-2334514f9c61 , which is CNN .. YOU CAN FIND DOCTOR COMMENTS IN ATTACHED AND REPORT AND MATLAB >
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