Abstract
Rotating machines are exposed to different faults such as shaft cracks, bearing failures, rotor misalignment, stator to rotor rub, etc. Therefore, turbo-generators, 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.
Highlights
Different faults such as shaft cracks, bearing failures, rotor misalignment, stator to rotor rub, etc., can affect the normal operation of rotating machinery
Vibrations of the rotor are measured at the bearings by proximity probes and amplified and analyzed at dynamic signal analyzers or specialized data acquisition devices equipped with dedicated software
The present paper introduces a new signal-based approach for rotor fault detection [48,49,50,51]
Summary
Different faults such as shaft cracks, bearing failures, rotor misalignment, stator to rotor rub, etc., can affect the normal operation of rotating machinery. Fast Fourier transform (FFT) [1,2,3,4,5,6,7,8] is applied This way, additional components at Fourier spectra increase [3,4,5,9,10,11,12] in the overall amplitude of vibrations or phase variations [1,7,8,11] can be observed and used as indications for rotor failure detection and warning. The method is based on auto-correlation and power spectral density functions of the vibration signals measured at the bearings of the rotating shaft. Provided numerical and experimental results confirm its high effectiveness and reliability when applied to shaft crack detection
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