信号(编程语言)
计算机科学
人工智能
模式识别(心理学)
计算机视觉
程序设计语言
作者
Shangbo Han,Longchao Yao,Dawei Duan,Jian Yang,Weihong Wu,Chunhui Zhao,Chenghang Zheng,Xiang Gao
标识
DOI:10.1016/j.isatra.2024.04.005
摘要
High-frequency signals like vibration and acoustic emission are crucial for condition monitoring, but their high sampling rates challenge data acquisition, especially for online monitoring. Our research developed a novel method for condition identification in undersampled signals using a modified convolutional neural network integrated with a signal enhancement approach. A frequency-domain filtering is applied to suppress similar sidebands and obtain more discriminative features of different conditions, followed by an interpolation-based upsampling in the time domain to restore the signal length and strengthen the low-frequency harmonic information. Enhanced signals are converted into two-dimensional grayscale images for neural network analysis. Tested on bearing datasets and real-world data from regenerative thermal oxidizer lift valve leakage, our method effectively extracts features from low-frequency signals, achieving over 95% fault identification accuracy.
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