Prediction of Vibration Characteristics of Mechanical Bearing Based on a Novel Grey Model

Sun, Yu; Yuan, Qiang; Zhou, Ruiping; Wen, Xiaofei

doi:10.1109/wcmeim48965.2019.00035

Cited by 1 publication

(1 citation statement)

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“…When combined with other intelligent algorithms, support vector machines can achieve precise fault identification even with limited fault samples. Huang et al [11] proposed an SVM model based on an optimized genetic algorithm, successfully identifying faults such as loosening of base screws and failure of buffer springs. The machine-learning-based fault-diagnosis method mainly involves two stages: feature extraction and fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi–Scale One–Dimensional Convolutional Neural Network for Intelligent Fault Diagnosis of Centrifugal Pumps

Geng,

Su,

Zhang

et al. 2023

JMSE

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Centrifugal pumps are susceptible to various faults, particularly under challenging conditions such as high pressure. Swift and accurate fault diagnosis is crucial for enhancing the reliability and safety of mechanical equipment. However, monitoring data under fault conditions in centrifugal pumps are limited. This study employed an experimental approach to gather original monitoring data (vibration signal data) across various fault types. We introduce a multi–scale sensing Convolutional Neural Network (MS–1D–CNN) model for diagnosing faults in centrifugal pumps. The network structure is further optimized by examining the impact of various hyperparameters on its performance. Subsequently, the model’s efficacy in diagnosing centrifugal pump faults has been comprehensively validated using experimental data. The results demonstrate that, under both single and multiple operating conditions, the model not only reduces reliance on manual intervention but also improves the accuracy of fault diagnosis.

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Section: Introductionmentioning

confidence: 99%