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首页> 外文会议>2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing >Fault diagnosis system of rotating machines using continuous wavelet transform and Artificial Neural Network
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Fault diagnosis system of rotating machines using continuous wavelet transform and Artificial Neural Network

机译：基于连续小波变换和人工神经网络的旋转机械故障诊断系统

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In this paper using a machine with a motor configuration that is connected with 3 discs. Performance of a machine can be known by analyzing the vibrations that occur in the machine. Vibration that occurs on the machine may be normal or abnormal. Abnormal vibrations on a machine can cause severe damage. This abnormal vibration can be caused by the mass distribution of rotation no longer exists in the centerline. This technique of identifying vibrations can use a combination of Continuous Wavelet Transform (CWT) and Artificial Neural Network (ANN) methods. The vibration signal is sampled to be transformed using CWT, so the data of Continuous Wavelet Coefficient (CWC) is obtained. The Feature Extraction method is used to extract the Continuous Wavelet Transform data into several types. Root Mean Square (RMS), Kurtosis, and Power Spectrum Density (PSD) are Feature Extraction types used as Artificial Neural Network inputs to identify abnormal vibrations in the machine. The Artificial Neural Network (ANN) intelligently classifies the fault from machine vibrations. CWT and ANN combinations are able to classify the damage by 99.72% accuracy.
机译：在本文中，使用具有3个光盘连接的电机配置的机器。通过分析机器中发生的振动可以知道机器的性能。机器上发生的振动可能是正常的还是异常的。机器上异常振动会导致严重损坏。这种异常振动可能是由中心线中不再存在的旋转质量分布引起的。这种识别振动的技术可以结合使用连续小波变换（CWT）和人工神经网络（ANN）方法。对振动信号进行采样，然后利用CWT进行变换，从而获得连续小波系数（CWC）的数据。特征提取方法用于将连续小波变换数据提取为几种类型。均方根（RMS），峰度和功率谱密度（PSD）是特征提取类型，用作人工神经网络输入以识别机器中的异常振动。人工神经网络（ANN）可根据机器振动对故障进行智能分类。 CWT和ANN组合能够以99.72％的准确度对损坏进行分类。

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