Studies on fault detection in rotary machine element with composite nanofluid as a lubricant

Abstract

A gearbox is considered to be one of the critical components of the machine. The gearboxes which are used in escalators, conveyors, rolling and sawmills, mining machinery, etc. have a high reduction ratio (in some cases 100:1 in a single stage). The failure of such a critical component leads to huge losses to the industry. Therefore, continuous monitoring of such a crucial component is essential to avoid any breakdown. The vibration analysis technique has been the most popular condition- monitoring technique among the researchers for last two decades and is widely being used in fault diagnosis of rotating components such as bearings, rotors, shafts, gears, pump impellers, etc. The early detection of a fault in the high-reduction gearbox is a challenging task as the fundamental frequencies in the spectrum are on the lower side and close together. The sliding mechanism of its operation and the presence of noise further complicate the task of fault detection. newline newlineIn this thesis, a vibration-based technique has been used for the diagnosis of faults in the high-reduction worm gearbox. The scheme starts with the acquisition of vibration signals from the worm gearbox test rig under different health conditions of the gearbox at different speeds using an accelerometer and data acquisition (DAQ) system. The acquired vibration signal is decimated, and an autoregressive minimum entropy deconvolution (AR-MED) is applied. The decimation process enhances the data handling capability. The AR-MED filter denoises the signal. The signal obtained is further processed for the local cepstrum (LC) to identify the quefrencies which provide the information regarding the period of repetition of impulses corresponding to the defect. The results of quefrencies validate the values of fault frequency for the faulty gear. A comparison with the autocorrelation LC and squared envelope spectrum kurtosis is presented to establish the effectiveness of the proposed scheme. The accuracy of the results of the proposed method is 99.27%. newline newline