Fault diagnosis in machine tools using selective regional correlation
Fault diagnosis in machine tools using selective regional correlation
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DOI：
10.1016/j.ymssp.2005.01.010
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发表时间：
2006-07
影响因子：
8.4
通讯作者：
Adam G. Rehorn;E. Sejdić;Jin Jiang
中科院分区：
文献类型：
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作者：
Adam G. Rehorn;E. Sejdić;Jin Jiang
本文研究了高精度加工中心的主轴定位伺服驱动器中的电刷卡死故障的检测和诊断，使用最近开发的时间-频率模式分类技术称为选择性区域相关（SRC）。结果表明，SRC是能够显着提高故障诊断的分辨率相比，传统的基于相关性的技术。该方法的性能进行了评估，使用三个时频变换技术：短时傅立叶变换（STFT），连续小波变换（CWT）和S变换。此外，三种不同的2D窗口用于隔离与SRC一起使用的特征：矩形（矩形）窗口，高斯窗口和Kaiser窗口。结果表明，SRC是一个很有前途的工具，机器状态监测（MCM）。
This paper investigates the detection and diagnosis of brush seizing faults in the spindle positioning servo drive of a high-precision machining centre using a recently developed time–frequency pattern classification technique known as selective regional correlation (SRC). It is shown that SRC is capable of significantly enhancing the resolution of fault diagnosis when compared to conventional correlation-based techniques. The performance of this approach is evaluated using three time–frequency transformation techniques: the short-time Fourier transform (STFT), continuous wavelet transform (CWT) and S-Transform. In addition, three different 2D windows are used to isolate features for use with SRC: a rectangular (boxcar) window, a Gaussian window and a Kaiser window. The results have indicated that SRC is a promising tool for machine condition monitoring (MCM).