Research on mixed-fault diagnosis of mine-used belt conveyor gearbox
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摘要: 针对基于振动信号分析的矿用带式输送机齿轮箱故障诊断方法存在不易处理混合故障信号的问题，提出了一种基于自组织映射网络的矿用带式输送机齿轮箱混合故障诊断方法。采用融入Shannon熵的小波阈值去噪方法对矿用带式输送机齿轮箱的标准多故障样本进行预处理，对预处理后的标准多故障样本建立高斯混合分布模型后，采用最大期望算法进行模型的参数估计，得到相应特征向量并输入自组织映射网络，自组织映射网络对不同混合故障类型的故障信号进行聚类和识别，从而判断故障类别。实验结果表明，该方法能有效诊断出矿用带式输送机齿轮箱的多故障混合信号对应的故障类别，整体诊断准确率为88%，在6种工况下诊断准确率为100%，为矿山机电设备的齿轮箱故障诊断提供了一种新方法。Abstract: In view of problem that fault diagnosis methods of mine-used belt conveyor gearbox based on vibration signal analysis are not easy to process mixed-fault signals, a new mixed-fault diagnosis method of mine-used belt conveyor gearbox based on self-organizing map network was proposed. The standard multi-fault samples of mine-used belt conveyor gearbox are pre-processed by wavelet threshold denoising method incorporating Shannon entropy，Gaussian mixture distribution model is established for the standard multi-fault samples after pre-treatment, and the expectation-maximization algorithm is used to estimate the parameters of the model to obtain corresponding feature vectors which are input into the self-organizing map network. At last, the fault signals of different fault types are clustered and identified by self-organizing map network to determine the fault category. The test results show that the method can effectively diagnose the fault type of mine-used belt conveyor gearbox, and the overall accuracy of the diagnostic method is 88%, and the accuracy under six conditions is 100%. It provides a new method for gearbox fault diagnosis of mine electromechanical equipment.
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期刊类型引用(2)
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