With the rapid development of industry 4.0, intelligent methods for detecting product quality have attached considerable interests. In traditional quality control methods, the process data is required to meet the requirements of independent and identical distribution, which limits the industrial applications. In this paper, a fast product quality detection method based on a clustering hypersphere model is proposed. First, to make the detection boundary more flexible, a simplified classification method based on k-means clustering is designed. Next, the smallest closed hypersphere is built for each subset, and its radius and center are calculated by a sequence minimum optimization algorithm. Then, by exploring the data distribution in the feature space, the accurate original image of the mirror sphere center can be obtained. Finally, compared with the traditional methods, the proposed method can achieve lower false-detection rate and faster detection speed according to the simulation and experimental results.

随着工业4.0的飞速发展，用于检测产品质量的智能方法引起了人们的极大兴趣。在传统的质量控制方法中，需要过程数据来满足独立且相同分布的要求，这限制了工业应用。本文提出了一种基于聚类超球模型的快速产品质量检测方法。首先，为了使检测边界更加灵活，一种基于k的简化分类方法-均值聚类被设计。接下来，为每个子集构建最小的封闭超球面，并通过序列最小优化算法计算其半径和中心。然后，通过探索特征空间中的数据分布，可以获得镜球中心的准确原始图像。最后，根据仿真和实验结果，与传统方法相比，该方法可以实现更低的误检率和更快的检测速度。