[672]风扇加速度计数据集(Accelerometer Data Set)

0、数据编号：672

1、数据名称：风扇加速度计数据集(Accelerometer Data Set)

2、数据来源：巴西圣保罗麦肯齐长老会大学

3、时间跨度：截止至2021-05-02

4、区域范围：

5、数据大小：3.65MB

6、数据格式：csv

7、数据简介：来自冷却器风扇叶片振动的加速度计数据。它可用于预测、分类需要振动分析的任务。生成此数据集是为了用于“使用人工神经网络预测电机故障时间”项目 。冷却器风扇上的叶片用于产生振动。这个冷却风扇连接了一个加速度计来收集振动数据。利用这些数据，可以使用人工神经网络预测电机故障时间。为了产生三种不同的振动场景，重量以三种不同的方式分布：1）‘红色’-正常配置：两个配重件位于相邻的叶片上；2) ‘蓝色’ – 垂直配置：两个配重片放置在叶片上形成 90° 角；3) ‘绿色’ – 相对配置：两个配重块位于相对的叶片上。示意图见论文图3。

使用的设备：

Akasa AK-FN059 12cm Viper cooling fan (Generate the vibrations)

MMA8452Q accelerometer (Measure vibration)

数据收集方法：设置 17 种不同转速的叶片，范围从冷却器最大速度的 20% 到 100%，间隔为 5%；分布配置三种重量的冷却器叶片中。Akasa AK-FN059 冷却器的最大转速为 1900 rpm，振动测量以 20 毫秒的频率收集，每种转速收集持续 1 分钟，生成 3000 条记录，共从仿真模型中收集 153,000 条振动记录。

变量如下:There are 5 attributes in the dataset: wconfid,pctid,x,y and z.

wconfid: Weight Configuration ID (1 – ‘red’ – normal configuration; 2 – ‘blue’ – perpendicular configuration; 3 – ‘green’ – opposite configuration)

pctid: Cooler Fan RPM Speed Percentage ID (20 means 20%, and so on).

x: Accelerometer x value.

y: Accelerometer y value.

z: Accelerometer z value.

英文原文:

Accelerometer data from vibrations of a cooler fan with weights on its blades. It can be used for predictions, classification and other tasks that require vibration analysis, especially in engines.This dataset was generated for use on ‘Prediction of Motor Failure Time Using An Artificial Neural Network’ project (DOI: 10.3390/s19194342). A cooler fan with weights on its blades was used to generate vibrations. To this fan cooler was attached an accelerometer to collect the vibration data. With this data, motor failure time predictions were made, using an artificial neural networks. To generate three distinct vibration scenarios, the weights were distributed in three different ways: 1) ‘red’ – normal configuration: two weight pieces positioned on neighboring blades; 2) ‘blue’ – perpendicular configuration: two weight pieces positioned on blades forming a 90° angle; 3) ‘green’ – opposite configuration: two weight pieces positioned on opposite blades. A schematic diagram can be seen in figure 3 of the paper.

Devices used:

Akasa AK-FN059 12cm Viper cooling fan (Generate the vibrations)

MMA8452Q accelerometer (Measure vibration)

Data collection method:

17 rotation speeds were set up, ranging from 20% to 100% of the cooler maximum speed at 5% intervals; for the three weight distribution configurations in the cooler blades. Note that the Akasa AK-FN059 cooler has 1900 rpm of max rotation speed.The vibration measurements were collected at a frequency of 20 ms for 1 min for each percentage, generating 3000 records per speed. Thus, in total, 153,000 vibration records were collected from the simulation model.

参考文献：

[1]calabrini Sampaio, Gustavo;

[2]Vallim Filho, Arnaldo R.d.A.;

[3]Santos da Silva, Leilton;

[4]Augusto da Silva, Leandro. 2019. Prediction of Motor Failure Time Using An Artificial Neural [5]Network. Sensors 2019, 19, 4342. DOI: 10.3390/s19194342