Liu Yanlei, Zhang Kun, Yang Miaorui, Zhang Xu, Xu Yonggang
Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing University of Technology, Beijing 100124, China.
DaHe Shuzhi Information Technology Division, DaHe Shuzhi (Quanzhou) Additive Co., Ltd., Quanzhou 362000, China.
Sensors (Basel). 2025 Mar 16;25(6):1848. doi: 10.3390/s25061848.
In view of the higher requirements of modern machinery for multi-sensor information acquisition and fusion technology, this paper proposes a novel multi-fusion analytic mode decomposition (MFAMD) method to separate and demodulate fault features in signals. In low-speed and heavy-load equipment, the signals collected by multiple sensors contain unknown and unequal fault features and interference. Quaternion-based frequency domain fusion technology and analytically based modal extraction technology can offer novel approaches to processing large data sets in parallel while handling lengthy signals and high sampling rates. The trend spectrum segmentation method based on quaternions optimizes the hysteresis of the binary frequency. The experimental signal verifies that the proposed method is suitable for low-speed and heavy-load bearing faults.
鉴于现代机械对多传感器信息采集与融合技术的更高要求，本文提出了一种新颖的多融合解析模式分解（MFAMD）方法，用于分离和解调信号中的故障特征。在低速重载设备中，多个传感器采集到的信号包含未知且不等的故障特征和干扰。基于四元数的频域融合技术和基于解析的模态提取技术，在处理长信号和高采样率时，可为并行处理大数据集提供新方法。基于四元数的趋势谱分割方法优化了二元频率的滞后现象。实验信号验证了所提方法适用于低速重载轴承故障。
