Electrical signals dataset from fixed-speed and variable-speed synchronous generators under healthy and faulty conditions

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2024-10-10 DOI:10.1016/j.dib.2024.111018

Rafael Noboro Tominaga , Luan Andrade Sousa , Rodolfo Varraschim Rocha , Renato Machado Monaro , Sérgio Luciano Ávila , Maurício Barbosa de Camargo Salles , Bruno Souza Carmo

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引用次数: 0

Abstract

Proper monitoring of rotating machines is responsible for the efficiency in detecting, diagnosing, or even prognosing failures. Effective monitoring can lead to increased economic viability of any equipment, as it may reduce costly repairs, decrease downtime, and increase safety. Knowing the behaviour of a machine promotes better monitoring of its operation and maintenance. Data-driven algorithms have been widely used to identify failures and predict the behaviour of machines and systems. The difficulty in obtaining reliable data to test strategies or methods for this purpose is well known. Our contribution is a set of electrical current data (time series data) from a rotating machine that generates electrical energy, generically called a power generator, in a laboratory. In this machine we have the possibility of, besides monitoring its healthy behaviour, causing internal defects that can reduce its efficiency and remaining useful life.

We highlight three key lines of study with the data available here: it is possible to apply data processing tools to make discoveries not evidenced in studies; test and compare new data-driven algorithms using public and reliable data; engineering lectures can use the dataset regarding the study of electrical machines and data driving methods.

The dataset contains information mainly about the voltage and current of generators when they are subject to internal faults. These faults include short circuits between turns of winding, short circuits between windings of the same phase, and short circuits between different phases.

This dataset has a wide variety of bench configurations. The dataset comes from real generators and allows the study of phenomena that are difficult to reproduce through analytical or computational models. The time series of electrical currents are raw, no preprocessing has been done. In fact, the signals contain natural noise from an industrial environment.

In this context, the main contribution of this work is to provide a public and reliable database, which helps to speed up the development of more efficient techniques for monitoring, diagnosis, and prognostics of the behaviour of rotating electrical machines.

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定速和变速同步发电机在健康和故障条件下的电信号数据集

对旋转机器进行适当的监控可以有效地检测、诊断甚至预报故障。有效的监控可以减少昂贵的维修费用、缩短停机时间并提高安全性，从而提高任何设备的经济可行性。了解机器的行为有助于更好地监控其运行和维护。数据驱动算法已被广泛用于识别故障以及预测机器和系统的行为。众所周知，很难获得可靠的数据来测试这方面的策略或方法。我们的研究成果是一组来自实验室中产生电能的旋转机器（一般称为发电机）的电流数据（时间序列数据）。在这台机器中，除了监测其健康行为外，我们还有可能造成内部缺陷，从而降低其效率和剩余使用寿命。我们强调了利用这里的数据进行研究的三个主要方向：可以应用数据处理工具来发现研究中没有的发现；利用公开和可靠的数据来测试和比较新的数据驱动算法；工程讲座可以利用该数据集来研究电机和数据驱动方法。这些故障包括绕组匝间短路、同相绕组间短路和不同相间短路。该数据集来自真实的发电机，可用于研究难以通过分析或计算模型重现的现象。电流的时间序列是原始的，没有经过任何预处理。在这种情况下，这项工作的主要贡献在于提供了一个公共和可靠的数据库，有助于加快开发更有效的技术，用于监测、诊断和预报旋转电机的行为。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.