Conditioning Monitoring of Gearbox Using Different Methods: A Review

Abstract

Gears are important element in a variety of industrial applications such as machine tool and gearboxes. An unexpected failure of the gear may cause significant economic losses. For that reason, fault diagnosis in gears has been the subject of intensive research. Vibration signal analysis has been widely used in the fault detection of rotation machinery. Fault diagnosis plays an important role in condition monitoring to enhance the machine time. In view of this, the present investigation focused on the development of Fault diagnosis system of gearboxes based on the vibration signatures and Artificial Neural Networks. In the present investigation to generate the vibration signatures an experimental set-up has been fabricated with sensing and measuring equipment. The prominent faults, wear, crack, broken tooth and insufficient lubrication of the gear were practically induced in the present investigation. Vibration signatures of the gearbox were collected by transmitting the motion at constant speed with gears having no fault, without applying any load. By inducing one fault at a time, vibration signatures were collected with different degrees of wear on a gear tooth, a gear with a broken tooth, tooth with crack and with insufficient lubrication. As the vibration data of maximum amplitudes was found to be inseparable, fault diagnosis based on this data was not possible. Five prominent statistical features were extracted based on data pertaining to maximum amplitudes of vibration and used fault diagnosis. Due overlapping of this data, it was decided to use ANN based fault diagnosis system for the present investigation. The set of statistical features were extracted based on data pertaining to maximum amplitudes of vibration and used them as input parameters to the ANN based fault diagnosis system designed. Keyword: Gearbox, Conditioning Monitoring, Acoustic Signals, Wavelet Transform, ANN.