Diagnostics of Bearings of Industrial Machines Using Real-Time Strain Gauge Analysis

Valeev, Anvar; Токарев, А. С.; Zotov, A. V.

doi:10.1109/icieam48468.2020.9111963

Cited by 1 publication

(1 citation statement)

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“…Rolling bearings, often referred to as 'the joints of the industry,' are crucial components of rotating machinery and find extensive applications across various industrial domains [1][2][3]. The least severe outcome of a bearing failure is the malfunction of equipment and subsequent economic losses; the most severe is the initiation of safety incidents and infliction of personal injuries.…”

Section: Introductionmentioning

confidence: 99%

Utilizing VQ-VAE for end-to-end health indicator generation in predicting rolling bearing RUL

Wang,

Zhang,

Zhu

et al. 2024

Meas. Sci. Technol.

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The prediction of the remaining useful life (RUL) of rolling bearings is a pivotal issue in 1industrial production. A crucial approach to tackling this issue involves transforming vibration 2signals into health indicators (HI) to aid model training. This paper presents an end-to-end HI 3construction method, vector quantised variational autoencoder (VQ-VAE), which addresses the need 4for dimensionality reduction of latent variables in traditional unsupervised learning methods such 5as autoencoder. Moreover, concerning the inadequacy of traditional statistical metrics in reflecting 6curve fluctuations accurately, two novel statistical metrics, mean absolute distance (MAD) and 7mean variance (MV), are introduced. These metrics accurately depict the fluctuation patterns in the 8curves, thereby indicating the model’s accuracy in discerning similar features. On the PMH2012 9dataset, methods employing VQ-VAE for label construction achieved lower values for MAD and MV. 10Furthermore, the ASTCN prediction model trained with VQ-VAE labels demonstrated commendable 11performance, attaining the lowest values for MAD and MV.

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Section: Introductionmentioning

confidence: 99%