Fault Detection Using Vibration Analysis and Particle Swarm Optimization of Rolling Element Bearing

EasyChair Preprint 9722

Abstract

The rolling element bearing's relevance and technical uses are clear, and it is subjected to various types of loading. The rolling bearing may crack because of fatigue loading. The presence of crack causes a change in physical properties of a bearing and thus reducing the stiffness of the rolling element bearing with invisible natural frequencies are being reduced. The essential parameters of vibration of bearing analysis are crack depth and location. The current study used Finite Element Analysis (FEA) and Particle Swarm Optimization (PSO) technology to create methodologies for fracture detection of a solitary crack in a cracked rolling bearing. Different crack location effects are taken into account, and the results are compared to different rolling bearing crack depths. Then Particle swarm optimization algorithm has been developed using the first three relative natural frequencies taken from FE analysis. For comparative study, both Standard PSO and APSO is used for crack diagnosis of the bearing. The feasibility of proposed PSO techniques is compared through error analysis. The research paper, the objective has been related to the design a Particle swarm optimization technique for the prediction of crack location and crack depth in a uniform cracked bearing.

Keyphrases: Bearing, PSO, Vibration analysis, crack, natural frequency

@booklet{EasyChair:9722,
  author    = {Rabinarayan Sethi and Bibhutibhusan Brahma and Krishna Chandra Patra},
  title     = {Fault Detection Using Vibration Analysis and Particle Swarm Optimization of Rolling Element Bearing},
  howpublished = {EasyChair Preprint 9722},
  year      = {EasyChair, 2023}}