Fault diagnosis in machine tools using selective regional correlation

作者：Rehorn AG; Sejdic E; Jiang J^*

来源：Mechanical Systems and Signal Processing, 2006, 20(5): 1221-1238.

DOI：10.1016/j.ymssp.2005.01.010

• machine condition monitoring
• selective regional correlation
• time-frequcncy transformations
• pattern classification

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 The results have indicated that SRC is a promising too] for machine condition monitoring (MCM).

• 出版日期2006-7