Mechanical and dry sliding wear analysis of porcelain reinforced SAE660 bronze bearing alloy composite fabricated by stir casting method

Rajak, Santosh Kumar; Kumar, Deepak; Seetharam, R.; Tandon, Puneet

doi:10.1016/j.matpr.2023.04.550

Cited by 2 publications

(1 citation statement)

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“…For sliding bearings, current research mainly focuses on issues such as rotor stability [20][21][22][23][24][25][26][27], calculation of oil film stiffness and damping [28][29][30][31], as well as material wear resistance performance [32][33][34], aiming to enhance the lubrication performance and wear resistance of bearings at the design stage. Although improving the lubrication performance and wear resistance of sliding bearings can effectively alleviate material wear problems, wear is inevitable during equipment startup, shutdown stages, and under the influence of some unexpected factors [35].…”

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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