Wang Bing, Li Huimin, Hu Xiong, Wang Cancan, Sun Dejian
Shanghai Maritime University, Shanghai, 201306, China.
Heliyon. 2024 Mar 13;10(6):e27986. doi: 10.1016/j.heliyon.2024.e27986. eCollection 2024 Mar 30.
In allusion to solve the issue of fault diagnosis for bearing and other rotatory machinery, a technique based on fined-grained multi-scale Kolmogorov entropy and whale optimized multi-class support vector machine (abbreviated as FGMKE-WOA-MSVM) is proposed. Firstly, vibration signals are decomposed by fine-grained multi-scale decomposition, and the Kolmogorov entropy of the sub-signals at different analysis scales is calculated as the multi-dimension feature vector, which quantitatively characterize the complexity of the signal at multi-scales. Aiming at the problem of sensitive parameters selection for multi-class support vector machine model (abbreviated as MSVM), the whale optimization algorithm (abbreviated as WOA) is introduced to optimize the penalty factor and kernel function parameter, and constructing optimal WOA-MSVM model. Finally, an instance analysis is carried out with Jiangnan University bearing datasets to verify the effectiveness and superiority of this technique. The results show that compared with different feature vectors and models such as K nearest neighbors (abbreviated as KNN) and Decision Tree (abbreviated as RF), the proposed technique is superior with fast computation speed and high diagnostic efficiency.
针对轴承及其他旋转机械的故障诊断问题，提出了一种基于细粒度多尺度柯尔莫哥洛夫熵和鲸鱼优化多类支持向量机的技术（简称为FGMKE-WOA-MSVM）。首先，通过细粒度多尺度分解对振动信号进行分解，并计算不同分析尺度下子信号的柯尔莫哥洛夫熵作为多维特征向量，该向量定量表征了信号在多尺度下的复杂性。针对多类支持向量机模型（简称为MSVM）敏感参数选择问题，引入鲸鱼优化算法（简称为WOA）对惩罚因子和核函数参数进行优化，构建最优的WOA-MSVM模型。最后，利用江南大学轴承数据集进行实例分析，验证该技术的有效性和优越性。结果表明，与不同特征向量和模型如K近邻（简称为KNN）和决策树（简称为RF）相比，所提技术具有计算速度快、诊断效率高的优势。
