Virtual Simulation Analysis and Experimental Study on Gear Fault Diagnosis Based on Wavelet Neural Network

Q1 Social Sciences HumanMachine Communication Journal Pub Date : 2010-04-24 DOI:10.1109/MVHI.2010.86
Xu Xiang, Z. Ruiping, Zhixiong Li
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引用次数: 8

Abstract

Due to the incipient gear fault vibration signal are covered by heavy noisy, it is difficult to diagnose the gear faults just according to the time or frequency spectrum of the signals. The comparison results of the virtual prototype model simulation and the experimental test also prove that the traditional Fast Fourier Transform Algorithm (FFT) analysis is not appropriate for the gear fault detection and identification. The Wavelet Back-Propagation (BP) Neural Network therefore was applied to extract the feature sets of the gear fault vibration data and classify the faults. At the first step, the wavelet analysis was employed to decompose the vibration data, and for each sample its energy of each sub-band was calculated and then treated as the input feature vector for the BP network training. By means of this approach the gear defection can be detected and recognized. The experiment test results show that the method based on wavelet BP network is available and reliable for gear fault diagnosis, and the monitoring and identification of different gear conditions, including normal, wear, and tooth broken, are accomplished with high classification accuracy.
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基于小波神经网络的齿轮故障诊断虚拟仿真分析与实验研究
由于早期齿轮故障振动信号被较重的噪声所覆盖,仅根据信号的时间或频谱来诊断齿轮故障是很困难的。虚拟样机模型仿真与实验测试的对比结果也证明了传统的快速傅立叶变换算法(FFT)分析方法不适用于齿轮故障的检测与识别。利用小波反向传播(BP)神经网络提取齿轮故障振动数据的特征集并进行故障分类。首先采用小波分析对振动数据进行分解,对每个样本计算其各子带能量,作为BP网络训练的输入特征向量。利用该方法可以对齿轮缺陷进行检测和识别。实验结果表明,基于小波BP网络的齿轮故障诊断方法是可行的、可靠的,能够对齿轮正常、磨损、断齿等不同状态进行监测和识别,分类精度较高。
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CiteScore
10.00
自引率
0.00%
发文量
10
审稿时长
8 weeks
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