A Novel 1D-Convolutional Spatial-Time Fusion Strategy for Data-Driven Fault Diagnosis of Aero-Hydraulic Pipeline Systems

Tongguang Yang, Guanchen Li, Tongyu Wang, Shengyou Yuan, Xueyin Yang (), Xiaoguang Yu and Qingkai Han
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Tongguang Yang: School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Guanchen Li: Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
Tongyu Wang: School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Shengyou Yuan: School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Xueyin Yang: School of Mechanical & Vehicle Engineering, Linyi University, Linyi 276012, China
Xiaoguang Yu: School of Mechanical Engineering and Automation, University of Science and Technology Liaoning Anshan, Anshan 114051, China
Qingkai Han: School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China

Mathematics, 2023, vol. 11, issue 14, 1-21

Abstract: Intelligent diagnosis of faults in an aero-hydraulic pipeline is important for condition monitoring of its systems. However, there are no more qualitative formulas or feature indicators to describe the faults of aero-hydraulic pipelines because of the complexity and diversity of aero-hydraulic pipeline systems, which leads to a very complex pipeline fault mechanism. In addition, although it is well known that the expression of interpretable and representable pipeline intelligent diagnosis models with pipeline fault characteristics are buried in high background noise and strong noise disturbance conditions in practical industrial scenarios, this has yet to be discussed. Inspired by the demand, this paper proposes a novel diagnosis strategy: the 1D-convolutional space-time fusion strategy for aero-engine hydraulic pipelines. Firstly, by optimizing the convolutional neural network and using it to design a one-dimensional convolutional neural network (1DCNN) with a wide input scale to expand the input field of perception, thereby obtaining more comprehensive spatial information of the pipeline data, which can effectively extract richer short sequence features. Secondly, a network of bidirectional gated recurrent Unit (Bi-GRU) is proposed, which integrates a short sequence of high-dimensional features for temporal information fusion, resulting in a certain degree of avoiding memory loss and gradient dispersion caused by the too-large step size. It is demonstrated that, for the noise signal and variable pressure signal, the fault identification accuracy approximated 95.9%, proving the proposed strategy’s robustness. By comparing with the other five methods, the proposed strategy has the ability to identify 10 different fault states in the aero-hydraulic pipeline with higher accuracy.

Keywords: fault diagnosis; aero-hydraulic pipeline; 1D-convolutional neural network; bidirectional gated recurrent unit; spatial-time fusion (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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