Abstract: In order to monitor the quality of production process of the process industry, a fault detection method based on data depth is proposed. The common mahalanobis depth and spatial depth are selected, and using Gaussian kernel function to generalize spatial depth in order to improve the sensitivity of spatial depth to position deviation. This method maps high-dimensional data to one-dimensional depth value by means of depth function （mahalanobis depth and kernelized spatial depth）, and then constructs asymptotic distribution by combining non-parametric rank statistics to make fault judgment. The effectiveness of the proposed method is verified through the Tennessee Eastman（TE） simulation experiment by referring to the two indicators of false alarm rate and detection efficiency and comparing with other methods.

Key words: fault detection, data depth, kernelized spatial depth, mahalanobis depth, rank statistic, TE process

摘要： 为了对过程工业的生产过程进行质量监控，提出了一种基于数据深度的故障检测方法。选取较为易用的马氏深度与空间深度，同时为了提高空间深度对位置偏离程度的敏感性，引入高斯核函数将其核化，借助深度函数（马氏深度、核空间深度）将高维过程数据映射成一维深度值，再结合非参数秩统计量构造渐近分布进行故障判断。通过田纳西-伊士曼（TE）仿真实验，参考误报警率和检测效率两个指标，并将故障检测效果与其他方法进行对比，验证了所提方法的有效性。

关键词: 故障检测, 数据深度, 核空间深度, 马氏深度, 秩统计量, TE过程