Gearbox fault diagnosis of rolling mills using multiwavelet sliding window neighboring coefficient denoising and optimal blind deconvolution
Gearbox fault diagnosis of rolling mills using multiwavelet sliding window neighboring coefficient denoising and optimal blind deconvolution
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DOI：
10.1007/s11431-009-0253-7
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发表时间：
2009-10-01
期刊：
影响因子：
--
通讯作者：
Liu Han
中科院分区：
文献类型：
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作者：
Yuan Jing;He ZhengJia;Liu Han
米尔斯轧机尤其是主传动齿轮箱的故障诊断对保证产品质量和长期安全运行具有重要意义。然而，在恶劣的环境下，有用的故障信息往往淹没在强背景噪声中。为此，提出了一种基于多小波滑动窗口邻域系数去噪和最优盲解卷积的米尔斯齿轮箱故障诊断新方法。新兴的多小波可以同时抓住重要的信号处理特性。由于多尺度和小波基函数，它们具有匹配各种特征的最大可能性。由于变速箱信号的周期性，建议采用滑动窗口进行局部阈值去噪，避免传统通用阈值技术的“矫枉过正”。同时，在每个滑动窗口中引入邻域系数去噪，充分考虑了系数间的相关性，有效地处理了含噪信号。因此，多小波滑动窗邻域系数去噪不仅能很好地提取齿轮箱故障特征，而且符合齿轮箱故障特征的本质。另一方面，进行最优盲反卷积，突出去噪后的特征，便于运营商识别。滤波器长度对于有效和有意义的结果至关重要。因此，最重要的滤波器长度选择的峰度的基础上进行了讨论，以充分发挥该技术的优势。将该方法应用于两个热连轧精米尔斯轧机齿轮箱故障诊断实例，并与多小波和单小波最优盲反卷积方法进行了比较。结果表明，该方法可以提高主传动齿轮箱的故障检测能力。
Fault diagnosis of rolling mills, especially the main drive gearbox, is of great importance to the high quality products and long-term safe operation. However, the useful fault information is usually submerged in heavy background noise under the severe condition. Thereby, a novel method based on multiwavelet sliding window neighboring coefficient denoising and optimal blind deconvolution is proposed for gearbox fault diagnosis in rolling mills. The emerging multiwavelets can seize the important signal processing properties simultaneously. Owing to the multiple scaling and wavelet basis functions, they have the supreme possibility of matching various features. Due to the periodicity of gearbox signals, sliding window is recommended to conduct local threshold denoising, so as to avoid the "overkill" of conventional universal thresholding techniques. Meanwhile, neighboring coefficient denoising, considering the correlation of the coefficients, is introduced to effectively process the noisy signals in every sliding window. Thus, multiwavelet sliding window neighboring coefficient denoising not only can perform excellent fault extraction, but also accords with the essence of gearbox fault features. On the other hand, optimal blind deconvolution is carried out to highlight the denoised features for operators' easy identification. The filter length is vital for the effective and meaningful results. Hence, the foremost filter length selection based on the kurtosis is discussed in order to full benefits of this technique. The new method is applied to two gearbox fault diagnostic cases of hot strip finishing mills, compared with multiwavelet and scalar wavelet methods with/without optimal blind deconvolution. The results show that it could enhance the ability of fault detection for the main drive gearboxes.