Abstract:

Adjustments in production decisions or changes from working statuses may lead to multi-modal production process. Commonly used data clustering methods have difficulty in parameter selection or need prior knowledge when recognizing multi-modal process. Therefore, a method is proposed that combines the kernel density estimation of heat diffusion determining density peak technology in the field of artificial intelligence with the Gaussian mixture model. It can effectively overcome the shortcomings of the current methods. The method first uses the kernel density estimation of heat diffusion determining density peak technology to estimate the local density of every data sample and its distance from higher local density to obtain the number of cluster centers and cluster the data set. Secondly, the characteristic parameters of different working conditions are obtained by using Gaussian mixture model: average, covariance and prior probability, so as to accurately describe the historical process of multiple operating conditions. Finally, two examples of Tennessee Eastman process and simulation data in literature were used for verification, and compared with K-means and Gaussian mixture model improved by F-J, it is proved that the proposed method can be more convenient and effective to accurately recognize the historical operating conditions.

Key words: multi-modal, clustering, model, parameter estimation, kernel density estimation

摘要：

在工业生产过程中，生产决策的调整或生产状况的变化会导致生产过程多模态化，常用的数据聚类方法进行工况识别时存在参数选取困难或需要先验知识等限制。基于此，提出一种将人工智能领域的热扩散核密度确定密度峰的技术与高斯混合模型相结合的方法，可有效克服目前方法的缺点。该方法首先利用热扩散核密度确定密度峰的技术估算每个数据点的密度及其与局部密度较大点的距离，获取数据集的聚类中心并完成聚类；其次，利用高斯混合模型获取不同工况的特征参数：平均值、协方差和先验概率，从而对多工况历史过程进行准确的描述；最后，利用文献中仿真数据和Tennessee Eastman过程两个案例进行验证，并与K-均值法和F-J改进的高斯混合模型进行比较，证明了本文提出方法可更加方便、有效地对历史工况进行准确识别。

关键词: 多模态, 聚类, 模型, 参数估值, 核密度估计