Recognition of Impact Load on Connecting-Shaft Rotor System Based on Motor Current Signal Analysis.

IF 3.4 3区 综合性期刊 Q2 CHEMISTRY, ANALYTICAL
Sensors Pub Date : 2024-10-31 DOI:10.3390/s24217008
Kun Zhang, Zhaojian Yang, Qingbao Bao, Jianwen Zhang
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引用次数: 0

Abstract

Impact loads affect the operational performance and safety life of rolling equipment's connecting-shaft rotor system, even causing faults and accidents. Therefore, recognizing and investigating impact loads is of great significance. Hence, a load recognition method based on motor current information is proposed in this paper to recognize impact loads on the connecting-shaft rotor system. First, the fast Fourier transform is used to obtain the frequency domain information for the motor's current response signal from the rotor system load recognition test. Consequently, the required load response information can be presented more clearly using the singular value decomposition method to remove the power frequency components in the current signal. Then, wavelet packet decomposition is performed on the signal to generate energy analysis feature vectors. A qualitative recognition of the impact load on the system is achieved by learning vector quantization neural networks; the resulting load recognition results are good. These findings indicate that using the motor current as the analysis signal can solve the problem of the difficult layout for traditional vibration sensors in rolling sites. The preprocessing and recognition method of the current response signal can recognize the impact load, confirming the applicability and feasibility of the proposed method.

基于电机电流信号分析的连轴转子系统冲击载荷识别。
冲击载荷会影响滚动设备连接轴转子系统的运行性能和安全寿命,甚至会导致故障和事故。因此,识别和研究冲击载荷意义重大。因此,本文提出了一种基于电机电流信息的负载识别方法,以识别连轴转子系统上的冲击负载。首先,利用快速傅里叶变换从转子系统负载识别测试中获取电机电流响应信号的频域信息。然后,利用奇异值分解法去除电流信号中的功率频率成分,从而更清晰地呈现所需的负载响应信息。然后,对信号进行小波包分解,生成能量分析特征向量。通过学习矢量量化神经网络,实现了对系统影响负载的定性识别;由此产生的负载识别结果良好。这些研究结果表明,使用电机电流作为分析信号可以解决轧制现场传统振动传感器布局困难的问题。对电流响应信号的预处理和识别方法可以识别冲击载荷,证实了所提方法的适用性和可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Sensors
Sensors 工程技术-电化学
CiteScore
7.30
自引率
12.80%
发文量
8430
审稿时长
1.7 months
期刊介绍: Sensors (ISSN 1424-8220) provides an advanced forum for the science and technology of sensors and biosensors. It publishes reviews (including comprehensive reviews on the complete sensors products), regular research papers and short notes. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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