The BT-SAM-Net: a new framework of end-to-end periodic time-series fault diagnosis for aero-pipelines systems

Abstract

Accurate and scientific recognition of aero-pipeline faults is of great practical significance for engine maintenance costs and downtime. Pipeline signals have a strong periodic time series correlation under strong pump source pressure pulsation stimulation. The features of the pipeline fault will be gradually transferred during the pump source pulsation cycle when fault occurs. However, very few studies have considered the correlation of features at pulsation period time points. Additionally, it is challenging to realize intelligent fault diagnosis of weak characteristics of pipeline faults due to the influence of external environmental noise and vibration-noise coupling of aero-engines. At the same time, enhancing the generalization ability of the aero-pipeline diagnosis model has become an urgent issue in academia and engineering. The Bi-Time-Self-Attention Mechanism (BT-SAM-Net), a newly created fault detection framework end-to-end time-series and anti-noise, is presented for the aero-engine hydraulic pipeline in order to close the aforementioned research gaps. The primary goal of the proposed framework is to accomplish end-to-end feature extraction and classification tasks by using the measured aero-pipeline raw data as the model input, without any data processing. Firstly, a two-way time series information fusion model is creatively designed, which is the first attempt to study and analyze the difference in time series correlation characteristics of different types of faults and different working conditions of aero-pipelines. Secondly, The BT-SAM-Net framework incorporates the self-attention mechanism as an optimization tool to enhance the ultimate decision-making accuracy of the framework. Thirdly, the BT-SAM-Net framework was compared with 7 other methods. The results show the superiority and stability by demonstrating the BT-SAM-Net framework can identify various aero-hydraulic fault states with greater accuracy.