<ul><li>Remaining Useful Life (RUL) estimation is crucial in Predictive Maintenance applications.</li><li>Traditional regression methods have struggled for high accuracy in this domain.</li><li>A hybrid approach combining Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks is proposed for RUL estimation.</li><li>The hybrid CNN-LSTM model achieves the highest accuracy, outperforming other methods.</li></ul>

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation

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