基于相空间重构和PSO-K-means的球磨机负荷状态识别方法

针对球磨机振动信号具有强随机性、非平稳性和非线性等内在特性导致负荷状态难以识别的问题，提出一种基于相空间重构和PSO-K-means的球磨机负荷状态识别方法。首先，利用改进前后的自相关系数算法对Lorenz与Rossler两种混沌时间序列进行数值模拟，得出延迟时间和嵌入维数精准有效的计算方法；其次，验证出球磨机筒体振动信号具有混沌特性后对其时间序列进行相空间重构，恢复出等价的混沌吸引子；接着，针对3种不同负荷状态下的相空间吸引子进行特征提取，分析了关联维数特征量的变化规律；最后，将关联维数作为特征向量输入PSO-K-means聚类模型中对球磨机负荷状态进行分类与识别。结果表明，PSO-K-means聚类模型在负荷状态识别时有较高的精准性，欠负荷、正常负荷、过负荷下识别精度分别为94.2%、96.3%、94.8%。以上结果证实了该方法能够实现对球磨机负荷状态的有效识别。

Load State Identification Method for Ball Mills Based on Phase Space Reconstruction and PSO-K-means

Aiming at the problem that it is difficult to identify the load state due to the inherent characteristics of ball mill vibration signal such as strong randomness, non-smoothness and non-linearity, a load state identification method for ball mill based on phase space reconstruction and PSO-K-means is proposed. Firstly, numerical simulation of two chaotic time series of Lorenz and Rossler used the improved before-and-after autocorrelation coefficient algorithm and derived an accurate and efficient method for calculating the delay time and embedding dimension. Then, feature extraction was performed for phase space attractors under three different load states, and the variation law of the associated dimensional feature quantity was analyzed. Finally, Classification and identification of ball mill load states by inputting the correlation dimensions as feature vectors into the PSO-K-means clustering model. The results show that the PSO-K-means clustering model has high accuracy in load state identification, with 94.2%, 96.3% and 94.8% accuracy under underload, normal load and overload, respectively. The above results confirm that the method can achieve effective identification of ball mill load states.