Agrupamento e classificação de séries temporais multivariadas com reconciliações de padrões

Abstract

The storage of a large amount of historical data in production processes has contributed to the development of techniques related to data mining (DM) and the extraction of useful knowledge about processes (Knowledge Discovery in Data bases, KDD). Although there are many studies related to Fault Detection and Diagnosis (FDD), few of them are based on grouping and pattern recognition in time series, especially in multivariate series. In addition, there are no work related to the recognition of patterns in time series that consider the process model as a constraint. This study proposes a new method for the recognition of patterns in uni and multivariate time series, based on the Fuzzy C-Means (FCM) algorithm, which directly considers the process dynamics in the clustering problem in order to guarantee the viability of the standards recognized. The proposed method is applied in two case studies, both related to clustering and recognition of patterns of abnormal operation (failures) and normal operation. The first case study is a Continuous Stirred Tank Reactor (CSTR), a well-known reference process used to evaluate control strategies and techniques for FDD. The second application involved a real industrial scenario comprising a commercial scale gas turbine located at the Rômulo Almeida thermoelectric plant (UTE), an integral part of the Companhia Brasileira de Petróleo park. The results show that the FCM algorithm and a typical metric of similarity between time series, based on the Principal Component Analysis (PCA), do not guarantee the recognition of patterns consistent with the process dynamics, even if good results are obtained classification and grouping. On the other hand, the results obtained from the reconciliation approaches proposed in this study show the obtaining of consistent and reconciled patterns with the dynamic reality of the process, without prejudice to the quality of the results of grouping and classification.