Contribution to gear pairs faults identification using mechanical vibration signal analysis techniques

Disertacija obrađuje problematiku pronalaženja pouzdane tehnike analize signala mehaničkih vibracija s ciljem identifikacije analiziranih tipova oštećenja zupčastih parova. U disertaciji je predložen postupak redukcije dimenzionalnosti obeležja primenom metode analize glavnih komponenata. Istraživanje uspešno demonstrira primenu naprednih tehnika procesiranja signala vibracija i inteligentnih metoda u vibrodijagnostici zupčastih parova te omogućava osobama koje nisu specijalisti iz oblasti dijagnostike da procene stanje zupčastog para. Predložena su najuniverzalnija obeležja u vibrodijagnostici zupčastih parova baziranih na signalima vibracija prikupljenih na kućištu zupčastog prenosnika.The dissertation deals with the issue of finding reliable signal analysis technique of mechanical vibrations to identify analyzed types of gear pair faults. The dissertation presents a method of reducing the dimensionality of features by using the method of principal components analysis. The study successfully demonstrates use of advanced signal processing techniques and artificial intelligent methods in diagnostics of gear pairs faults and allows engineers who are not specialists in the field to assess the condition of gear pair using vibration signals. The most universal features in diagnostics of gear pairs based on vibration signals collected on the gearbox housing are proposed

Contribution to gear pairs faults identification using mechanical vibration signal analysis techniques

Disertacija obrađuje problematiku pronalaženja pouzdane tehnike analize signala mehaničkih vibracija s ciljem identifikacije analiziranih tipova oštećenja zupčastih parova. U disertaciji je predložen postupak redukcije dimenzionalnosti obeležja primenom metode analize glavnih komponenata. Istraživanje uspešno demonstrira primenu naprednih tehnika procesiranja signala vibracija i inteligentnih metoda u vibrodijagnostici zupčastih parova te omogućava osobama koje nisu specijalisti iz oblasti dijagnostike da procene stanje zupčastog para. Predložena su najuniverzalnija obeležja u vibrodijagnostici zupčastih parova baziranih na signalima vibracija prikupljenih na kućištu zupčastog prenosnika.The dissertation deals with the issue of finding reliable signal analysis technique of mechanical vibrations to identify analyzed types of gear pair faults. The dissertation presents a method of reducing the dimensionality of features by using the method of principal components analysis. The study successfully demonstrates use of advanced signal processing techniques and artificial intelligent methods in diagnostics of gear pairs faults and allows engineers who are not specialists in the field to assess the condition of gear pair using vibration signals. The most universal features in diagnostics of gear pairs based on vibration signals collected on the gearbox housing are proposed

Gearbox faults feature selection and severity classification using machine learning

The most widely used technique for gearbox fault diagnosis is still vibration analysis. The need for gearbox condition monitoring in an automated process is essential and there is still a problem with the selection of features that best describe a fault or its severity level. For this purpose, multiple-domain vibration signals statistic features are extracted through time and frequency domain by postprocessing of raw time signal, time-synchronous average signal, frequency spectra and cepstrum. Five different datasets are considered with different levels of fault analyzing gear chipped and a missing tooth, gear root crack, and gear tooth wear under stable running speed and load. A preliminary experimental study of a single stage test bench gearbox was performed in order to test feature sensitivity to type and level of fault in the process of clustering and classification. Selected features were finally processed using an artificial neural network classifier

Application of the DC Offset Cancellation Method and S Transform to Gearbox Fault Diagnosis

In this paper, the direct current (DC) offset cancellation and S transform-based diagnosis method is verified using three case studies. For DC offset cancellation, correlated kurtosis (CK) is used instead of the cross-correlation coefficient in order to determine the optimal iteration number. Compared to the cross-correlation coefficient, CK enhances the DC offset cancellation ability enormously because of its excellent periodic impulse signal detection ability. Here, it has been proven experimentally that it can effectively diagnose the implanted bearing fault. However, the proposed method is less effective in the case of simultaneously present bearing and gear faults, especially for extremely weak bearing faults. In this circumstance, the iteration number of DC offset cancellation is determined directly by the high-speed shaft gear mesh frequency order. For the planetary gearbox, the application of the proposed method differs from the fixed-axis gearbox, because of its complex structure. For those small fault frequency parts, such as planet gear and ring gear, the DC offset cancellation’s ability is less effective than for the fixed-axis gearbox. In these studies, the S transform is used to display the time-frequency characteristics of the DC offset cancellation processed results; the performances are evaluated, and the discussions are given. The fault information can be more easily observed in the time-frequency contour than the frequency domain