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一种用于使用光纤布拉格光栅传感器的状态监测系统中消除加性噪声的改进谱减法方法。

An Improved Spectral Subtraction Method for Eliminating Additive Noise in Condition Monitoring System Using Fiber Bragg Grating Sensors.

作者信息

Liu Qi, Yu Yongchao, Han Boon Siew, Zhou Wei

机构信息

Schaeffler Hub for Advanced Research at NTU, 61 Nanyang Dr, ABN-B1b-11, Singapore 637460, Singapore.

School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore.

出版信息

Sensors (Basel). 2024 Jan 11;24(2):443. doi: 10.3390/s24020443.

DOI:10.3390/s24020443
PMID:38257536
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10821454/
Abstract

The additive noise in the condition monitoring system using fiber Bragg grating (FBG) sensors, including white Gaussian noise and multifrequency interference, has a significantly negative influence on the fault diagnosis of rotating machinery. Spectral subtraction (SS) is an effective method for handling white Gaussian noise. However, the SS method exhibits poor performance in eliminating multifrequency interference because estimating the noise spectrum accurately is difficult, and it significantly weakens the useful information components in measured signals. In this study, an improved spectral subtraction (ISS) method is proposed to enhance its denoising performance. In the ISS method, a reference noise signal measured by the same sensing system without working loads is considered the estimated noise, the same sliding window is used to divide the power spectrums of the measured and reference noise signals into multiple frequency bands, and the formula of spectral subtraction in the standard SS method is modified. A simulation analysis and an experiment are executed by using simulated signals and establishing a vibration test rig based on the FBG sensor, respectively. The statistical results demonstrate the effectiveness and feasibility of the ISS method in simultaneously eliminating white Gaussian noise and multifrequency interference while well maintaining the useful information components.

摘要

在使用光纤布拉格光栅(FBG)传感器的状态监测系统中,包括白高斯噪声和多频干扰在内的附加噪声,对旋转机械的故障诊断有显著的负面影响。谱减法(SS)是处理白高斯噪声的有效方法。然而,由于准确估计噪声谱困难,且会显著削弱测量信号中的有用信息成分,谱减法在消除多频干扰方面表现不佳。在本研究中,提出了一种改进的谱减法(ISS)来提高其去噪性能。在改进的谱减法中,将由相同传感系统在无工作负载情况下测量的参考噪声信号视为估计噪声,使用相同的滑动窗口将测量噪声信号和参考噪声信号的功率谱划分为多个频段,并对标准谱减法中的谱减法公式进行修改。分别通过使用模拟信号和基于光纤布拉格光栅传感器建立振动试验台进行了仿真分析和实验。统计结果表明,改进的谱减法在同时消除白高斯噪声和多频干扰的同时,能很好地保留有用信息成分,具有有效性和可行性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/036ca087831d/sensors-24-00443-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/6a02ba971968/sensors-24-00443-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/184efdc203e1/sensors-24-00443-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/1c9721644926/sensors-24-00443-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/4298013fb889/sensors-24-00443-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/2270363c4e7b/sensors-24-00443-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/d98cbc41e499/sensors-24-00443-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/13e5292075a7/sensors-24-00443-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/0a4c30bea1b8/sensors-24-00443-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/b1ee0baddcbe/sensors-24-00443-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/036ca087831d/sensors-24-00443-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/6a02ba971968/sensors-24-00443-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/184efdc203e1/sensors-24-00443-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/1c9721644926/sensors-24-00443-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/4298013fb889/sensors-24-00443-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/2270363c4e7b/sensors-24-00443-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/d98cbc41e499/sensors-24-00443-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/13e5292075a7/sensors-24-00443-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/0a4c30bea1b8/sensors-24-00443-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/b1ee0baddcbe/sensors-24-00443-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/10821454/036ca087831d/sensors-24-00443-g010.jpg

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