Method of shaft crack detection based on squared gain of vibration amplitude
Rafał Grądzki , Zbigniew Kulesza , Błażej Bartoszewicz
AbstractRotating machines are exposed to different faults such as shaft cracks, bearing failures, rotor misalignment, stator to rotor rub, etc. Therefore, turbogenerators, aircraft engines, compressors, pumps, and many other rotating machines should be constantly diagnosed to warn about the probable appearance of a possible rotor failure. Unfortunately, despite the ongoing work on various rotor fault detection methods, there are still very few techniques that can be considered as reliable and applicable in practical problems. The difficulty lies in the fact that usually the fault introduces very subtle local changes in the overall structure of the rotor. The symptoms of these changes must be isolated and extracted from a wide spectrum of vibration data obtained from sensors measuring the vibrations of the machine. The measured data are usually disturbed with some noise or other disturbances, and that is why the detection of a possible rotor fault is even more difficult. The paper presents a new rotor fault detection method. The method is based on a new diagnostic model of rotor signals and external disturbances. The model utilizes auto-correlation functions of measured rotor’s vibrations. By proper processing of the measured vibration data, the influence of environmental disturbances is completely compensated and reliable indications of the possible rotor fault are obtained. The method has been tested numerically using the finite element model of the rotor and then verified experimentally at the shaft crack detection test rig. The results are presented in a readable graphical form and confirm high sensitivity and reliability of the method.
|Journal series||Nonlinear Dynamics, ISSN 0924-090X, e-ISSN 1573-269X, (N/A 140 pkt)|
|Publication size in sheets||0.95|
|Keywords in English||auto-correlation function, power spectral density function, signal processing, fault detection, damage map|
|ASJC Classification||; ; ; ; ;|
|Internal identifier||ROC 19-20|
|Score||= 140.0, 12-02-2020, ArticleFromJournal|
|Publication indicators||: 2016 = 1.523; : 2018 = 4.604 (2) - 2018=4.363 (5)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.