Centrifugal Pump Diagnosis Using Stacked Denoising Auto Encoders Optimized by Imporved Golden Jackal Optimization

ZHANG Maohuan; ZHANG Weijie; XU Shushan

doi:10.3969/j.issn.1000-1158.2025.05.16

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Acta Metrologica Sinica ›› 2025, Vol. 46 ›› Issue (5) : 730-737. DOI: 10.3969/j.issn.1000-1158.2025.05.16

Centrifugal Pump Diagnosis Using Stacked Denoising Auto Encoders Optimized by Imporved Golden Jackal Optimization

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Abstract

In order to solve the problem that the stacked denoise auto encoders （SDAE） with unreasonable superparameters reduces the fault diagnosis accuracy of centrifugal pump， a golden jackal optimization algorithm （GJO） was selected to optimized SDAE superparameters. Considering the fact that the performance of GJO algorithm is greatly affected by the prey escape energy， an adaptive escape energy strategy was designed and the adaptive golden jackal optimization algorithm （AGJO） was obtained. Use AGJO optimize SDAE superparameters， a centrifugal pump fault diagnosis method based on AGJO-SDAE was proposed. Typical examples of centrifugal pump fault diagnosis showed that compared with other methods， the average diagnostic accuracy was at least 1.03% higher， the standard deviation was at least 0.007 lower， and the time spent was at least 4.87 s lower. Meanwhile， under the noise intensity of 2 dB， 8 dB and 14 dB， the relative attenuation rates of diagnostic accuracy when compared with other methods were reduced by 0.21%， 1.01% and 0.94%， respectively.

Key words

fault diagnosis / stacked denoising auto encoders / golden jackal optimization / adaptive escape energy / centrifugal pump

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ZHANG Maohuan , ZHANG Weijie , XU Shushan. Centrifugal Pump Diagnosis Using Stacked Denoising Auto Encoders Optimized by Imporved Golden Jackal Optimization[J]. Acta Metrologica Sinica. 2025, 46(5): 730-737 https://doi.org/10.3969/j.issn.1000-1158.2025.05.16

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