EMD神经网络和深度神经网络在自调心轴承故障诊断中的应用

IF 0.6 4区物理与天体物理 Q4 ACOUSTICS

Archives of Acoustics Pub Date : 2023-07-26 DOI:during daytime. 10.24425/122364

Narendiranath Babu Thamba, Arun Aravind, Abhishek Rakesh, Mohamed Jahzan, D Rama Prabha

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引用次数: 2

摘要

调心滚子轴承是工业机械的重要组成部分。事先对轴承可能发生的各种故障进行适当的分析和预测，有助于提高轴承的工作寿命。本研究旨在开发一种新的方法来分析自调心轴承的各种故障，并利用人工神经网络(ANN)和深度神经网络(DNN)对故障进行自动分类。振动数据被收集为六个不同的

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Application of EMD ANN and DNN for Self-Aligning Bearing Fault Diagnosis

Self-aligning roller bearings are an integral part of the industrial machinery. The proper analysis and prediction of the various faults that may happen to the bearing beforehand contributes to an increase in the working life of the bearing. This study aims at developing a novel method for the analysis of the various faults in self-aligning bearings as well as the automatic classification of faults using artificial neural network (ANN) and deep neural network (DNN). The vibration data is collected for six different

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来源期刊

Archives of Acoustics 物理-声学

CiteScore

1.80

自引率

11.10%

发文量

审稿时长

6-12 weeks

期刊介绍： Archives of Acoustics, the peer-reviewed quarterly journal publishes original research papers from all areas of acoustics like: acoustical measurements and instrumentation, acoustics of musics, acousto-optics, architectural, building and environmental acoustics, bioacoustics, electroacoustics, linear and nonlinear acoustics, noise and vibration, physical and chemical effects of sound, physiological acoustics, psychoacoustics, quantum acoustics, speech processing and communication systems, speech production and perception, transducers, ultrasonics, underwater acoustics.