  In this paper, a Robust Multi-branch Deep learning-based system for remaining
useful life (RUL) prediction and condition operations (CO) identification of
rotating machines is proposed. In particular, the proposed system comprises
main components: (1) an LSTM-Autoencoder to denoise the vibration data; (2) a
feature extraction to generate time-domain, frequency-domain, and
time-frequency based features from the denoised data; (3) a novel and robust
multi-branch deep learning network architecture to exploit the multiple
features. The performance of our proposed system was evaluated and compared to
the state-of-the-art systems on two benchmark datasets of XJTU-SY and
PRONOSTIA. The experimental results prove that our proposed system outperforms
the state-of-the-art systems and presents potential for real-life applications
on bearing machines.
