A MOVING WINDOW APPROACH TO IDENTIFICATION OF PATTERNS IN MULTIVARIATE TIME SERIES: APPLICATION TO THE MULTIVARIATE FAULT DETECTION OF ELECTRICAL SUBMERSIBLE PUMPS

Abstract The paper describes a multivariate time series pattern recognition method based on reference windows and used for the detection of fault patterns of electric submersible pumps caused by scales formed during production process in petroleum wells. Through a “moving window” strategy, the algorithm finds and selects reference windows in a long time series and computes the similarity between each selected window and the reference one (smaller time series) using the Euclidean distance. This method can simultaneously get the results of fault detection and fault diagnosis in a monitoring process. Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.References: 1. Abdalla, R., Samara, H., Perozo N., Paz-Carvajal C., and Jaeger P. Machine Learning Approach for Predictive Maintenance of the Electrical Submersible Pumps (ESPs). ACS Omega 2022, 7, 21, 17641–17651, https://doi.org/10.1021/acsomega.1c05881 2. 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