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国外文献 > 1.轴承 > 7. 智能方法与计算 > 神经网络

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标 题： A CNN-based Method for Multisensor and Multiscale Fault Diagnosis of Marine Electric Thruster Bearings under Noise Conditions
基于CNN的多传感器与多尺度噪声条件下船舶电动推进器轴承故障诊断方法
作 者： Xueqin Zhang
Chenxing Sheng
Wu Ouyang
Chengpeng Wan
Xiaowei Dong
Jinhua Xiao
Dongling Xu
关键词： Marine electric thruster bearings  [船舶电动推进器轴承]
Fault diagnosis  [ 故障诊断]
Convolutional neural network  [ 卷积神经网络]
Multisensor fusion  [ 多传感器融合]
Multiscale features extraction  [ 多尺度特征提取]
出版年： 2023
文集名： 7th International Conference on Transportation Information and Safety (ICTIS 2023)
第七届交通运输信息与安全国际会议（ICTIS 2023）
会议名： 7th International Conference on Transportation Information and Safety (ICTIS 2023)
第七届交通运输信息与安全国际会议（ICTIS 2023）
届 次： 7th
摘 要： Bearings are indispensable supporting parts of the marine electric thruster (MET). Due to long-term work in harsh marine environments, bearings are more prone to failures. However, the diagnosis of MET bearings using vibration signals is a difficult task since the collected signal is nonlinear and nonstationary, and its fault features are easily swallowed by environmental noise. This paper proposes a novel multisensor and multiscale convolutional neural networks (MM-CNN) method to extract different scale features under low signal-to-noise ratios and diagnose the faults of MET bearings. Firstly, the principal component analysis is introduced to fuse multiple signals so as to reduce redundant information. Secondly, a multiscale convolutional module is designed for the feature extraction at different scales by four groups of convolution kernels with different dilate rates. The experimental results on the MET bearing dataset demonstrate that the proposed method outperforms the other state-of-the-art deep learning methods in terms of accuracy and antinoise ability.

轴承是船舶电动推进器（MET）不可或缺的支撑部件。由于长期在恶劣的海洋环境中运行，轴承更容易发生故障。然而，利用振动信号诊断MET轴承故障是一项艰巨的任务，因为采集到的信号具有非线性和非平稳性，其故障特征极易被环境噪声淹没。本文提出一种新颖的多传感器多尺度卷积神经网络（MM-CNN）方法，在信噪比较低的情况下提取不同尺度的特征，并对MET轴承故障进行诊断。首先，引入主成分分析法融合多个信号，以减少冗余信息； 其次，设计了一个多尺度卷积模块，通过四组不同扩张率的卷积核，在不同尺度上实现特征提取。在MET轴承数据集上的实验结果表明，所提方法在准确性和抗噪能力方面均优于其他当前最先进的深度学习方法。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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