Multivariate statistical techniques have been widely applied in industrial processes to detect abnormal behaviors, while their performance could be unsatisfactory due to insufficient extraction of complex data characteristics. A method named Orthogonal transformed statistics Mahalanobis distance (OTSMD) is developed to handle this issue. As a feature-based method, OTSMD simultaneously considers various data characteristics through monitoring statistical features of process variables. Orthogonal transformed components (OTCs) are first calculated to capture variable correlation, and a set of statistical features is determined to extract other crucial characteristics, especially for the process non-stationarity. Statistical features of OTCs, which reveals implied process information, are continuously obtained using a sliding window, and a Mahalanobis distance index is utilized for fault detection. Compared with existing methods, OTSMD extracts data characteristics more comprehensively with a lower dimension, making it more effective in monitoring various faults. The results are illustrated through a numerical example, and two chemical industrial processes.

多元统计技术已广泛应用于工业过程中以检测异常行为，但由于复杂数据特征提取不足，其性能可能并不令人满意。开发了一种称为正交变换统计马哈拉诺比斯距离（OTSMD）的方法来处理这个问题。作为一种基于特征的方法，OTSMD通过监测过程变量的统计特征同时考虑各种数据特征。首先计算正交变换分量（OTC）以捕获变量相关性，并确定一组统计特征以提取其他关键特征，特别是对于过程非平稳性。使用滑动窗口连续获得OTC的统计特征，揭示隐含的过程信息，并利用马哈拉诺比斯距离指数进行故障检测。与现有方法相比，OTSMD以更低的维度更全面地提取数据特征，使其更有效地监控各种故障。结果通过数值示例和两个化学工业过程进行说明。