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A New Artificial Neural Network-Based Failure Determination System for Electric Motors
Arabian Journal for Science and Engineering ( IF 1.711 ) Pub Date : 2021-04-07 , DOI: 10.1007/s13369-021-05594-8
S. Bicakci, M. Coramik, H. Gunes, H. Citak, Y. Ege

In this study, a new measurement system was developed to determine failures and to define the level of failure that may occur in bearings and rotor bearings or in foot of motor in single phase capacitor start motor. In the system, the vibratory operation of the motor is provided by connecting different screws on the motor’s rotor mounted flywheel or by gradually removing the nut bolts of motor foot. The VB3 vibration sensor outputs were recorded to the computer with LabVIEW program at 1 ms intervals for one minute. The changing characteristics of sensor output for each experiment had more than one frequency component; therefore, Fast Fourier Transform (FFT) was performed for determining such components. When the obtained FFT graphs were analyzed, it was determined that the vibrations had harmonics of 50 Hz and its multiples; and it was observed that the frequency and amplitude values of first 5 harmonics could be used for determining the presence, type and level of failure but there was a nonlinear relation between each other. Therefore, 2 different artificial neural networks (ANN) customized separately were developed for determining the type and rate of the failure of motor. 80%, 10% and 10% of available data were reserved for training, testing and verification, respectively, and the ANN was trained. Accuracy degree for the ANN in the estimations following the training stage was calculated as R = 0.97–0.98. Furthermore, the results of ANN were compared with the results obtained using Sequential Minimal Optimization, Naive Bayes (NB) and J48 algorithms; and it was determined that the accuracy degree of ANN was higher. After this, a program was developed in MATLAB in order to work 2 ANNs with highest success together. Lastly, a system consisting of Raspberry Pi and a 7″ LCD screen, similar to the multimedia system in cars, was created to use at industrial applications.



中文翻译:

基于新的基于人工神经网络的电动机故障确定系统

在这项研究中,开发了一种新的测量系统来确定故障并确定在单相电容器启动电动机中轴承和转子轴承或电动机脚中可能发生的故障等级。在该系统中,通过连接安装在电机转子上的飞轮上的不同螺钉或逐渐卸下电机底脚的螺母螺栓来提供电机的振动操作。VB3振动传感器的输出以LabVIEW程序以1 ms的间隔记录到计算机中,持续一分钟。每个实验中传感器输出的变化特性具有一个以上的频率分量。因此,执行快速傅立叶变换(FFT)来确定此类分量。对获得的FFT图进行分析时,确定振动具有50Hz的谐波及其倍数。可以观察到,前5次谐波的频率和幅度值可用于确定故障的存在,类型和级别,但彼此之间存在非线性关系。因此,开发了两种分别定制的不同人工神经网络(ANN),用于确定电动机的故障类型和故障率。分别为培训,测试和验证保留了80%,10%和10%的可用数据,并对ANN进行了培训。训练阶段后的估计中ANN的准确度计算为 开发了两种分别定制的不同人工神经网络(ANN),用于确定电动机的故障类型和故障率。分别为培训,测试和验证保留了80%,10%和10%的可用数据,并对ANN进行了培训。训练阶段后的估计中ANN的准确度计算为 开发了两种分别定制的不同人工神经网络(ANN),用于确定电动机的故障类型和故障率。分别为培训,测试和验证保留了80%,10%和10%的可用数据,并对ANN进行了培训。训练阶段后的估计中ANN的准确度计算为R  = 0.97–0.98。此外,将ANN的结果与使用顺序最小优化,朴素贝叶斯(Naive Bayes,NB)和J48算法获得的结果进行了比较;并确定了人工神经网络的准确度较高。此后,在MATLAB中开发了一个程序,以便可以一起成功使用2个ANN。最后,创建了一个由Raspberry Pi和一个7英寸的LCD屏幕组成的系统,类似于汽车中的多媒体系统,该系统可用于工业应用。

更新日期:2021-04-08
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