Real‐time assessment on health state for bearing based on parallel encoder‐decoder observer

Abstract

Bearings are foundational supporting components in diverse mechanical systems, essential for the reliable operation of these systems through real‐time monitoring and precise health state assessment. However, vibration signals from bearings in practical equipment often contain excessive noise and redundant information, complicating health state assessment. To address this challenge, this paper proposes a neural network‐based method named parallel encoder‐decoder (PED). This method features a parallel architecture that combines the long short‐term memory network and the temporal convolutional network for the encoder, along with a self‐attention module for the decoder. PED is adept at learning the temporal representations hidden in original signals and filtering vibration signals to remove noise and redundant information. Additionally, a multi‐objective loss function is developed to enhance the prediction results. A normalized Mahalanobis distance‐based metric is then employed to compare residual signals during bearing operation with those under normal conditions. The case study evaluates the PED observer's proficiency in accurately predicting vibration signals and assessing the performance of health indicator curves, demonstrating the proposed PED observer's superiority over conventional networks.