Update README.md

README.md

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 This repository contains a CNN-LSTM model trained to predict the Remaining Useful Life (RUL) of turbofan engines based on time-series sensor data. The model employs a hybrid architecture where a 1D Convolutional Neural Network (CNN) acts as a feature extractor to identify local patterns from sensor readings within a time window, and a Long Short-Term Memory (LSTM) network then processes these features to capture long-term temporal dependencies indicative of engine degradation. The model takes a window of 40 time steps with 22 features as input and outputs a single value representing the predicted RUL in cycles.
 
-This particular model checkpoint was trained exclusively on the FD001 sub-dataset from the NASA CMAPSS collection. This dataset represents a single operating condition and a single fault mode (HPC degradation). As a result, the model is highly specialized for this specific scenario. Its performance is not guaranteed and is expected to be significantly lower on other CMAPSS sub-datasets (FD002, FD003, FD004), which involve multiple operating conditions and/or fault modes. For broader applications, retraining or using a model trained on the more complex datasets is recommended.
+This particular model checkpoint was trained exclusively on the FD001 sub-dataset from the NASA CMAPSS collection. This is done to showcase how this model works with the proposed XAI Framework on this specific condition/machine. 
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+This dataset represents a single operating condition and a single fault mode (HPC degradation).