One of the most important aspects of PHM is remaining useful life (RUL) estimation. This paper proposes a hybrid deep learning-based approach for RUL estimation. The hybrid method is developed using a combination of long short-term memory and convolutional neural networks. The effectiveness of the hybrid method is validated using three engine fleets from turbofan engines simulation datasets.