Time-Sensor Domain Data Decomposition and Analysis for Fault Diagnosis of Cutting Tools
Time-Sensor Domain Data Decomposition and Analysis for Fault Diagnosis of Cutting Tools
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DOI：
10.1109/icarcv57592.2022.10004293
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Yufei Gui;Z. Lang;Zepeng Liu;Yunpeng Zhu;H. Laalej
中科院分区：
文献类型：
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作者：
Yufei Gui;Z. Lang;Zepeng Liu;Yunpeng Zhu;H. Laalej
在本研究中，提出了一种新的时间和传感器域的数据分解和分析框架进行故障诊断的刀具。针对多传感器数据驱动的刀具状态监测系统计算量过大的问题，从数据层面将原始信号压缩成一组小得多的时域和传感器域数据，解决了这一问题。时域分量的利用消除了环境噪声对原始信号的影响。同时，传感器域分量的引入揭示了多个传感器之间的相关关系。实验结果验证了该方法的有效性，并说明了时域和传感器域特征与原始信号特征相比的优势。
In the present study, a novel time and sensor domain data decomposition and analysis framework is proposed to perform fault diagnosis of cutting tools. The problem of excessive computation burden existing in multi-sensor data-driven tool condition monitoring (TCM) system is resolved at the data level by compressing raw signals into a significantly smaller set of time and sensor domain data. The utilisation of the time domain components eliminates the influence of environmental noise on raw signals. Meanwhile, the introduction of the sensor domain components reveals the correlation relationship within multiple sensors. Experimental studies are conducted to verify the effectiveness of the proposed approach and illustrate the advantages of the time and sensor domain features compared with raw signal features.