Liu Yunyi, He Wenjun, Pan Tao, Qin Shuxian, Ruan Zhaokai, Li Xiangcheng
The Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning 530004, China.
School of Computer and Electronic Information, Guangxi University, Nanning 530004, China.
Sensors (Basel). 2025 Mar 20;25(6):1944. doi: 10.3390/s25061944.
In industrial polishing, the sensor on the polishing motor needs to extract accurate signals in real time. Due to the insufficient real-time performance of Variational Mode Decomposition (VMD) for signal extraction, some studies have proposed the Recursive Sliding Variational Mode Decomposition (RSVMD) algorithm to address this limitation. However, RSVMD can exhibit unstable performance in strong-interference scenarios. To suppress this phenomenon, a Parameter-Optimized Recursive Sliding Variational Mode Decomposition (PO-RSVMD) algorithm is proposed. The PO-RSVMD algorithm optimizes RSVMD in the following two ways: First, an iterative termination condition based on modal component error mutation judgment is introduced to prevent over-decomposition. Second, a rate learning factor is introduced to automatically adjust the initial center frequency of the current window to reduce errors. Through simulation experiments with signals with different signal-to-noise ratios (SNR), it is found that as the SNR increases from 0 dB to 17 dB, the PO-RSVMD algorithm accelerates the iteration time by at least 53% compared to VMD and RSVMD; the number of iterations decreases by at least 57%; and the RMSE is reduced by 35% compared to the other two algorithms. Furthermore, when applying the PO-RSVMD algorithm and the RSVMD algorithm to the Inertial Measurement Unit (IMU) for measuring signal extraction performance under strong interference conditions after the polishing motor starts, the average iteration time and number of iterations of PO-RSVMD are significantly lower than those of RSVMD, demonstrating its capability for rapid signal extraction. Moreover, the average RMSE values of the two algorithms are very close, verifying the high real-time performance and stability of PO-RSVMD in practical applications.
在工业抛光中，抛光电机上的传感器需要实时提取准确信号。由于变分模态分解（VMD）在信号提取方面实时性能不足，一些研究提出了递归滑动变分模态分解（RSVMD）算法来解决这一局限性。然而，RSVMD在强干扰场景下可能表现出不稳定的性能。为了抑制这种现象，提出了一种参数优化的递归滑动变分模态分解（PO-RSVMD）算法。PO-RSVMD算法通过以下两种方式对RSVMD进行优化：第一，引入基于模态分量误差突变判断的迭代终止条件，以防止过度分解。第二，引入速率学习因子，自动调整当前窗口的初始中心频率，以减少误差。通过对不同信噪比（SNR）信号的仿真实验发现，随着SNR从0 dB增加到17 dB，与VMD和RSVMD相比，PO-RSVMD算法的迭代时间至少加快了53%；迭代次数至少减少了57%；均方根误差（RMSE）比其他两种算法降低了35%。此外，在抛光电机启动后强干扰条件下，将PO-RSVMD算法和RSVMD算法应用于惯性测量单元（IMU）进行测量信号提取性能测试时，PO-RSVMD的平均迭代时间和迭代次数明显低于RSVMD，表明其具有快速信号提取能力。而且，两种算法的平均RMSE值非常接近，验证了PO-RSVMD在实际应用中的高实时性能和稳定性。
