结合相邻系数TQWT去噪在旋转机械故障诊断中的应用

发布时间：2018-03-14 02:52

本文选题：可调品质因子小波变换(TQWT)　切入点：信号去噪　出处：《齐齐哈尔大学学报(自然科学版)》2017年01期 　论文类型：期刊论文

【摘要】：加强对旋转机械的故障诊断对提高产品质量和长期安全运行具有重要意义。然而在故障诊断的过程中,有用的弱特征通常是具有强大的噪声背景,从而增加了特征提取的难度。为此,提出一种使用相邻系数可调品质因子小波变换(TQWT)的诊断方法,这种新兴可调品质因子小波变换与传统恒定的品质因子小波变换相比,具有可以随着信号的振荡使品质因子一致的优良性质。与此同时,采用相邻系数降噪是为了避免对约定逐项阈值技术过度矫正。结合这两种方法的优点,提出的去噪方法比其它方法更具有实用性和有效性。这种方法被应用到一个带用外圈缺陷滚动轴承和齿轮箱故障的检测,处理结果表明,该方法能够成功地识别故障特征,表明该方法比传统的小波阈值去噪方法更有效。
[Abstract]:It is very important to strengthen the fault diagnosis of rotating machinery to improve the quality of products and to run safely for a long time. However, in the process of fault diagnosis, the useful weak feature is usually with strong noise background. Thus, the difficulty of feature extraction is increased. Therefore, a diagnosis method using adjacent coefficient adjustable quality factor wavelet transform (TQWT) is proposed. The new adjustable quality factor wavelet transform is compared with the traditional constant quality factor wavelet transform. It has the excellent property that the quality factor can be consistent with the oscillation of the signal. At the same time, the adjacent coefficient denoising is used to avoid overcorrection of the agreed item-by-item threshold technique. The proposed denoising method is more practical and effective than other methods. This method is applied to the fault detection of rolling bearings and gearboxes with outer ring defects. The processing results show that the proposed method can successfully identify fault features. It shows that this method is more effective than the traditional wavelet threshold denoising method.
【作者单位】：亳州职业技术学院;
【基金】：安徽省2014年高等学校省级自然科学研究重点项目(KJ2014A170) 安徽省2015年高等学校重点教学研究项目(2015jyxm535) 安徽省2013年质量工程项目“特色专业——机电一体化技术”(2013tszy066)
【分类号】：TH17

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