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首页> 外文期刊>International Journal of Intelligent Systems and Applications >A Neural Network Based Motor Bearing Fault Diagnosis Algorithm and its Implementation on Programmable Logic Controller
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A Neural Network Based Motor Bearing Fault Diagnosis Algorithm and its Implementation on Programmable Logic Controller

机译：基于神经网络的电机轴承故障诊断算法及其在可编程控制器中的实现

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This research aims to test the feasibility of Programmable Logic Controller implementation of an Artificial Neural Network based bearing fault diagnosis using vibration datasets. The main drawback of using a Programmable Logic Controller along with an Artificial Neural Network is that it does not support the parallel nature of neural networks. This drawback is not significant for relatively small applications like bearing diagnosis that involve very short execution time. In this paper, a three layer multilayer perceptron backpropagation neural network is trained using Levenberg-Marquardt training algorithm with vibration dataset consisting of four bearing status classes: normal, outer race way fault, inner race way fault and rolling element (ball) fault. Time-frequency domain and time domain input features were considered in this research. Both approaches have performed well during simulation phase. But the time-frequency feature extraction approach was observed to take too long scan cycle time to be implemented in real-time. This is due to the computationally intensive nature of Fast Fourier Transform algorithm involved during feature extraction. The time domain approach is proved to be feasible for Programmable Logic Controller implementation. The time domain input features used for neural network training were root mean square, variance, kurtosis and negative log likelihood values. The average performance obtained during simulation with 10-fold cross validation performance estimator was an error of 7.9 x10-4. The performance tests of Programmable Logic Controller implementation resulted in 100% bearing fault detection rate.
机译：这项研究旨在测试使用振动数据集的基于人工神经网络的轴承故障诊断的可编程逻辑控制器实现的可行性。将可编程逻辑控制器与人工神经网络一起使用的主要缺点是它不支持神经网络的并行特性。对于相对较小的应用（例如轴承诊断，涉及非常短的执行时间），此缺点并不明显。在本文中，使用Levenberg-Marquardt训练算法对三层多层感知器反向传播神经网络进行了训练，其振动数据集由四个轴承状态类别组成：正常，外圈故障，内圈故障和滚动元件（球）故障。在这项研究中考虑了时频域和时域输入特征。两种方法在仿真阶段均表现良好。但是观察到时频特征提取方法花费的扫描周期时间太长，无法实时实施。这是由于特征提取过程中涉及的快速傅立叶变换算法的计算量大。实践证明，时域方法对于可编程逻辑控制器的实施是可行的。用于神经网络训练的时域输入特征是均方根，方差，峰度和负对数似然值。使用10倍交叉验证性能估算器进行仿真时获得的平均性能误差为7.9 x10-4。通过对可编程逻辑控制器实施的性能测试，轴承故障检测率达到了100％。

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