In spite of the advances in strategies involving clustering and pattern recognition in time series, there are no approaches capable of directly associating the recognized patterns with the dynamic behavior of the process investigated. This paper presents a new approach involving pattern reconciliation in the clustering of time series. The method is based on Fuzzy C-Means and considers the process dynamics as a soft constraint in order to ensure the feasibility of the recognized patterns. The first case study comprises the diagnosis of abnormal operation of a non-isothermal Continuous Stirred Tank Reactor (CSTR), a benchmark system used for the assessment of Fault Detection and Diagnosis (FDD) techniques. The second comprises a real industrial scenario which involves the recognition of starting patterns in a gas turbine for fault detection purposes. The results show that the proposed method is able to recognize feasible patterns preserving the quality of clustering and classification.

尽管在时间序列上涉及聚类和模式识别的策略取得了进步，但尚无能够将识别的模式与所研究过程的动态行为直接关联的方法。本文提出了一种在时间序列聚类中涉及模式协调的新方法。该方法基于模糊C均值，并将过程动力学视为软约束，以确保所识别模式的可行性。第一个案例研究包括诊断非等温连续搅拌釜反应器（CSTR）的异常运行，该反应器是用于评估故障检测和诊断（FDD）技术的基准系统。第二个包括实际的工业场景，该场景涉及为故障检测目的而识别燃气轮机中的启动模式。