Adaptive thresholding and coordinate attention-based tree-inspired network for aero-engine bearing health monitoring under strong noise

Abstract

In engineering, due to strong noise interference, it is a challenging issue for decision-making of the neural network to accurately detect healthy conditions of aero-engine bearings. As such, a hierarchical health monitoring model, termed adaptive thresholding and coordinate attention-based tree-inspired network (ATCATN) is developed for the health monitoring of aero-engine bearings under strong background noise. The ATCATN integrates the advantages of the adaptive thresholding and coordinate attention-based network (ATCAN) as well as a tree structure-based method, and it is not merely a straightforward integration of two models. First, the ATCAN is constructed as the backbone network, where the coordinate attention module is developed to process one-dimensional vibration signals; meanwhile, the designed deep residual adaptive thresholding module is introduced to extract significant features from vibration signals with strong noise. Second, a two-layer tree-inspired decision layer, consisting of seed and leaf nodes, is developed to reconstruct the output layer of the ATCAN. Finally, the trained ATCATN is more consistent with maintenance cognition involving multilevel decision information by the progressive determination of fault locations and fault sizes. The experimental results and comparative analysis with seven advanced methods on two aero-engine bearing datasets show that the developed model has the following advantages: (a) model robustness under strong noise; (b) more accurate diagnosis result and (c) hierarchical diagnosis with fault locations and fault sizes. Based on the experimental results, the ATCATN model demonstrates high accuracy in progressively identifying fault locations and sizes of aero-engine bearings even under strong noise interference, which is beneficial for formulating maintenance strategies and aligns with the basic understanding of maintenance.