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

TL;DR

This paper introduces a robust multi-branch deep learning system that combines denoising, feature extraction, and multi-modal analysis to improve RUL prediction and operational condition identification for rotating machines, outperforming existing methods.

Contribution

The paper presents a novel multi-branch deep learning architecture integrated with an LSTM-Autoencoder for denoising and multi-domain feature extraction, enhancing RUL prediction accuracy.

Findings

Outperforms state-of-the-art systems on benchmark datasets

Effective in real-life bearing machine applications

Demonstrates robustness against noisy vibration data

Abstract

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.