Abstract Dissimilarity algorithm has been widely used for timely identifying changes of data distribution in fine scale while many distance-based monitored indexes still stay inside the normal region. However, it ignores the information of temporal distribution, thus fails to monitor operating conditions and process dynamics separately. In order to detect incipient faults sensitively and also provide more benefiting process comprehension, concurrent static and dynamic dissimilarity analytics based on slow feature analysis technique (CSDDISSIM) is developed in this work. The distributions of process status and dynamics are evaluated in fine scale. First, static features and their temporal counterpart are extracted, in which slow and fast varying information is separated for distribution evaluation. Then the changes of both static and dynamic distributions are checked and the monitoring policy is developed to distinguish different statues, including normal, static deviation, dynamic abnormality, and concurrent deviations. In this way, the industrial process status can be captured with a beneficial interpretation. The practical utility and efficacy of the proposed method are illustrated in the application to a real thermal power plant process.

中文翻译：

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并行静态和动态差异分析，用于对过程数据分布进行精细评估

摘要 相异性算法被广泛用于及时识别细尺度数据分布的变化，而许多基于距离的监测指标仍停留在正常区域内。然而，它忽略了时间分布的信息，因此无法分别监控操作条件和过程动态。为了灵敏地检测早期故障并提供更有益的过程理解，本文开发了基于慢特征分析技术（CSDDISSIM）的并发静态和动态相异分析。过程状态和动态的分布以精细的尺度进行评估。首先，提取静态特征及其时间对应物，其中将慢速和快速变化的信息分离以进行分布评估。然后检查静态和动态分布的变化，并制定监控策略以区分不同的状态，包括正常、静态偏差、动态异常和并发偏差。通过这种方式，可以通过有益的解释来捕获工业过程状态。在实际热电厂过程的应用中说明了所提出方法的实际效用和功效。