ESPset

Published: 11 April 2024| Version 1 | DOI: 10.17632/m268jsw339.1
Contributors:
,
,
, Lucas Henrique Sousa Mello,

Description

A real-world dataset for vibration-based fault diagnosis of electric submersible pumps used on offshore oil exploration. The ESPset dataset is a collection of vibration signals acquired from accelerometers strategically attached onto the components of Electrical Submersible Centrifugal Pumps (ESP). An ESP belong to a class of equipment used in the extraction and exploration of oil and gas subject to severe working conditions. An ESP system consists of a coupled set of one or more electric motors, pumps and protectors. # spectrum.csv This csv file is a matrix of 6032 lines and 12103 columns, whose values are float numbers separated by a ';'. Each line of this file contains the spectrum of a single vibration signal collected from a sensor at a specific test condition of the ESP. Each value is the amplitude in inches per second (velocity) at a specific frequency. Each signal is normalized by the rotation frequency in which the ESP operates, in such a way that the amplitude with respect to the rotation frequency is always at the same position for all signal arrays. # features.csv This csv file of 6033 lines (one line for each signal + a header), contains some features and the labels for all signals: - esp_id: The id of the ESP. - label: The classification label. Let X be defined as the rotation frequency of the ESP. Each feature is defined as: median(8,13): Median of the amplitudes in the interval (8% X, 13% X); median(98,102): Median of the amplitudes in the interval (98% X, 102% X); a: Coefficient a of the exponential regression of type e^(a*A+b) where A is an array of equally separated relative frequencies up to 0.4, excluding zero. Example: A=(0.01, 0.02, ..., 0.39, 0.4). b: Coefficient b of the exponential regression of type e^(a*A+b) where A is an array of equally separated relative frequencies up to 0.4, excluding zero. Example: A=(0.01, 0.02, ..., 0.39, 0.4). peak1x: Amplitude in X; peak2x: Amplitude in 2X; rms(98,102): Root mean square of the amplitudes in the interval (98% X, 102% X). For more information and code, take a look at https://github.com/NINFA-UFES/ESPset

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Institutions

Universidade Federal do Espirito Santo

Categories

Machine Learning, Vibration Analysis, Discrete Fourier Transform, Fault Diagnosis

Funding

Petrobras

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