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In this paper, a Robust Multi-branch Deep learning-based system for remaininguseful life (RUL) prediction and condition operations (CO) identification ofrotating machines is proposed. In particular, the proposed system comprisesmain components: (1) an LSTM-Autoencoder to denoise the vibration data; (2) afeature extraction to generate time-domain, frequency-domain, andtime-frequency based features from the denoised data; (3) a novel and robustmulti-branch deep learning network architecture to exploit the multiplefeatures. The performance of our proposed system was evaluated and compared tothe state-of-the-art systems on two benchmark datasets of XJTU-SY andPRONOSTIA. The experimental results prove that our proposed system outperformsthe state-of-the-art systems and presents potential for real-life applicationson bearing machines.

Robust-MBDL: A Robust Multi-branch Deep Learning Based Model for  Remaining Useful Life Prediction and Operational Condition Identification of  Rotating Machines