A Novel Prognostic Method for Wear of Sliding Bearing Based on SFENN

Dai, Jingzhou; Tian, Ling

doi:10.1007/978-981-99-6504-5_19

Cited by 1 publication

(1 citation statement)

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“…König et al [37] predicted the macroscopic wear amount of sliding bearings based on the Archard model and Fleischer model. Dai and Tian [38] introduced a sequential hybrid model of neural network and finite element for predicting wear in sliding bearings. In general, these methods simulate and predict bearing wear based on static conditions and cannot integrate with online data.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Diagnostic Method for Wear Depth of Sliding Bearings Based on MGCNN

Dai,

Tian,

Chang

2024

Machines

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Sliding bearings are vital components in modern industry, exerting a crucial influence on equipment performance, with wear being one of their primary failure modes. In addressing the issue of wear diagnosis in sliding bearings, this paper proposes an intelligent diagnostic method based on a multiscale gated convolutional neural network (MGCNN). The proposed method allows for the quantitative inference of the maximum wear depth (MWD) of sliding bearings based on online vibration signals. The constructed model adopts a dual-path parallel structure in both the time and frequency domains to process bearing vibration signals, ensuring the integrity of information transmission through residual network connections. In particular, a multiscale gated convolution (MGC) module is constructed, which utilizes convolutional network layers to extract features from sample sequences. This module incorporates multiple scale channels, including long-term, medium-term, and short-term cycles, to fully extract information from vibration signals. Furthermore, gated units are employed to adaptively assign weights to feature vectors, enabling control of information flow direction. Experimental results demonstrate that the proposed method outperforms the traditional CNN model and shallow machine learning model, offering promising support for equipment condition monitoring and predictive maintenance.

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Section: Introductionmentioning

confidence: 99%