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2 Archives of Acoustics

2 2020

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Study of Effect of Linear Tip Relief Modification in Power Transmission Efficiency of Spur Gears

Thamba Narendiranath Babu, Tambare Abhijeet Mukund, Ananthanarayanan Krishna Caitanya, Duraiswamy Rama Prabha, Thangavelu Shankar, Easwara Pillai Raj Kumar, Mangalaraja Ramalinga Viswanathan

Archives of Acoustics

2020

Vol. 45, No. 2

271--282

This paper explores the influence of linear gear tip relief modification on power transmission efficiency. In real time applications gears experience transmission error (TE) during operation which increases noise and vibration and also results in increased tooth profile deformation during operation of the gear. By providing tip relief profile modification this TE can be decreased. Using MATLAB for computation and ANSYS for the simulation of deformation, stress, strain, life, and factor of safety results for the gear assemblies are obtained. Deformation results are used for the computation change in power transmission efficiency followed by the modal and harmonic analysis of the gears and gear assemblies to determine change in the first mode of natural frequency.

Fault Analysis of Worm Gear Box Using Symlets Wavelet

Thamba Narendiranath Babu, Thatikonda Venkata Kiran Kamesh, Nutakki Sathvik, Duraiswamy Rama Prabha, Mohammed Noor, Wahab Razia Sultana, Mangalaraja Ramalinga Viswanathan, Manivannan Ajay Vannan

Archives of Acoustics

2020

Vol. 45, No. 3

521--540

This research highlights the vibration analysis on worm gears at various conditions of oil using the experimental set up. An experimental rig was developed to facilitate the collection of the vibration signals which consisted of a worm gear box coupled to an AC motor. The four faults were induced in the gear box and the vibration data were collected under full, half and quarter oil conditions. An accelerometer was used to collect the signals and for further analysis of the vibration signals, MATLAB software was used to process the data. Symlet wavelet transform was applied to the raw FFT to compare the features of the data. ANN was implemented to classify various faults and the accuracy is 93.3%.