期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 21, 期 2, 页码 906-919出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2006.02.005
关键词
gear tooth fault diagnosis; gear tooth fillet cracks; gear tooth spalls
This paper proposes the use of the minimum entropy deconvolution (MED) technique to enhance the ability of the existing autoregressive (AR) model based filtering technique to detect localised faults in gears. The AR filter technique has been proven superior for detecting localised gear tooth faults than the traditionally used residual analysis technique. The AR filter technique is based on subtracting a regular gearmesh signal, as represented by the toothmesh harmonics and immediately adjacent sidebands, from the spectrum of a signal from one gear obtained by the synchronous signal averaging technique (SSAT). The existing AR filter technique performs well but is based on autocorrelation measurements and is thus insensitive to phase relationships which can be used to differentiate noise from impulses. The MED technique can make a use of the phase information by means of the higher-order statistical (HOS) characteristics of the signal, in particular the kurtosis, to enhance the ability to detect emerging gear tooth faults. The experimental results presented in this, paper validate the superior performance of the combined AR and MED filtering techniques in detecting spalls and tooth fillet cracks in gears. (c) 2006 Elsevier Ltd. All rights reserved.
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