Abstract
Condition monitoring and fault diagnosis play the most important role in industrial applications. The gearbox system is an essential component of mechanical system in fault identification and classification domains. In this paper, we propose a new technique which is based on the Fast-Kurtogram method and Self Organizing Map (SOM) neural network to automatically diagnose two localized gear tooth faults: a pitting and a crack. These faults could have very different diagnostics; however, the existing diagnostic techniques only indicate the presence of local tooth faults without being able to differentiate between a pitting and a crack. With the aim to automatically diagnose these two faults, a dynamic model of an electromechanical system which is a simple stage gearbox with and without defect driven by a three phase induction machine is proposed, which makes it possible to simulate the effect of pitting and crack faults on the induction stator current signal. The simulated motor current signal is then analyzed by using a Fast-Kurtogram method. Self-organizing map (SOM) neural network is subsequently used to develop an automatic diagnostic system. This method is suitable for differentiating between a pitting and a crack fault.
Highlights
Gearbox based induction motors are one of the most popular mechanisms in industrial machinery
We have proposed our approach based on motor current signal analysis (MCSA), spectral kurtosis and the Self-organizing map (SOM) neural network for diagnosis, which provides valuable information on the presence and differentiate of gear tooth defects
On the other hand the comparison of these two spectrums: the spectrum obtained in the presence of pitting fault Fig. 7(a) and that obtained in the presence of cracked tooth Fig. 7(b) failed to differentiate these two defects. To overcome this problem we propose to use the Fast-Kurtogram method
Summary
Gearbox based induction motors are one of the most popular mechanisms in industrial machinery. All these reduction in gear tooth stiffness have been studied by many authors [11,12,13,14] and these studies show the correlation between the severity of the damage and the reduction in stiffness, but which give as the same characteristics of signature for each fault For this reason, we have proposed our approach based on MCSA, spectral kurtosis and the SOM neural network for diagnosis, which provides valuable information on the presence and differentiate of gear tooth defects. Self-organizing map (SOM) neural network is used to develop an automatic diagnostic system This method is suitable for differentiate automatically between a pitting tooth and a crack tooth fault
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