School of Automation and Software Engineering, Shanxi University, Taiyuan 030013, China.
School of Mathematical Sciences, Shanxi University, Taiyuan 030006, China.
Rev Sci Instrum. 2021 Dec 1;92(12):125102. doi: 10.1063/5.0065197.
It is difficult to effectively distinguish the key information of non-stationary dynamic signals in many engineering applications, such as fault detection, geological exploration, and logistics transportation. To deal with this problem, a classification and recognition algorithm based on variational mode decomposition (VMD) and the Support Vector Machine (SVM) optimized by the Whale Optimization Algorithm (WOA) optimization model is first proposed in this study. The algorithm first applies VMD to decompose the non-stationary time-domain signals into multiple variational intrinsic mode functions (VIMFs). Then, it calculates the correlation coefficient between each mode and the original signals and conducts signal reconstruction by sorting the VIMFs. On the base of this, it performs modal filtering on the non-stationary signals according to the correlation coefficients between the reconstructed signal and the original signal. Subsequently, the WOA is used to optimize two key parameters of the SVM. Finally, the optimization model is exploited to classify and recognize the impact and vibration of non-stationary signals. A series of simulations and experiments for the algorithm is carried out and analyzed deeply. The comparative test results indicate that the classification and recognition method for non-stationary signals based on VMD and WOA-SVM (VMD-WOA-SVM) proposed in this paper converges faster and recognizes the key information of non-stationary dynamic signals more accurately with a recognition precision of 96.66%.
在许多工程应用中，如故障检测、地质勘探和物流运输，很难有效地区分非平稳动态信号的关键信息。针对这个问题，本研究首先提出了一种基于变分模态分解（VMD）和鲸鱼优化算法（WOA）优化模型的支持向量机（SVM）分类识别算法。该算法首先应用 VMD 将非平稳时域信号分解为多个变分固有模态函数（VIMFs）。然后，计算每个模态与原始信号之间的相关系数，并通过对 VIMFs 进行排序来进行信号重建。在此基础上，根据重构信号与原始信号之间的相关系数对非平稳信号进行模态滤波。随后，使用 WOA 优化 SVM 的两个关键参数。最后，利用优化模型对非平稳信号的冲击和振动进行分类和识别。对算法进行了一系列的模拟和实验，并进行了深入的分析。比较测试结果表明，本文提出的基于 VMD 和 WOA-SVM 的非平稳信号分类识别方法（VMD-WOA-SVM）收敛速度更快，对非平稳动态信号的关键信息识别更准确，识别精度达到 96.66%。
