Rolling bearings fault diagnosis based on two-stage signal fusion and deep multi-scale multi-sensor network

IF 6.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS ISA transactions Pub Date : 2024-11-01 DOI:10.1016/j.isatra.2024.08.033
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Abstract

In order to realize high-precision diagnosis of bearings faults in a multi-sensor detection environment, a fault diagnosis method based on two-stage signal fusion and deep multi-scale multi-sensor networks is proposed. Firstly, the signals are decomposed and fused using weighted empirical wavelet transform to enhance weak features and reduce noise. Secondly, an improved random weighting algorithm is proposed to perform a second weighted fusion of the signals to reduce the total mean square error. The fused signals are input into the deep multi-scale residual network, the feature information of different convolutional layers is extracted through dilated convolution, and the features are fused using pyramid theory. Finally, the bearings states are classified according to the fusion features. Experiment results show the effectiveness and superiority of this method.
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基于两级信号融合和深度多尺度多传感器网络的滚动轴承故障诊断。
为了在多传感器检测环境下实现轴承故障的高精度诊断,提出了一种基于两阶段信号融合和深度多尺度多传感器网络的故障诊断方法。首先,利用加权经验小波变换对信号进行分解和融合,以增强弱特征并降低噪声。其次,提出一种改进的随机加权算法,对信号进行第二次加权融合,以减少总均方误差。融合后的信号输入深度多尺度残差网络,通过扩张卷积提取不同卷积层的特征信息,并利用金字塔理论对特征进行融合。最后,根据融合特征对轴承状态进行分类。实验结果表明了这种方法的有效性和优越性。
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来源期刊
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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