Development of Rolling Bearing Health Diagnosis and Prediction System Using MEMS Accelerometer Vibration Sensing Module

This work implements an in-house fabricated, cost-effective MEMS piezoelectric accelerometer module to acquire the vibration data for fault diagnosis of a rolling bearing in an electric motor. Choosing the right combination of data pre-processing method (Ensemble Empirical Mode Decomposition, EEMD)...

Full description

Saved in:
Bibliographic Details
Published in2022 IEEE 35th International Conference on Micro Electro Mechanical Systems Conference (MEMS) pp. 446 - 449
Main Authors Satija, Jyoti, Huang, Po-Wen, Singh, Somnath, Shen, Tung, Chen, Hung-Yu, Li, Sheng-Shian
Format Conference Proceeding
LanguageEnglish
Published IEEE 09.01.2022
Subjects
Accelerometers
Conferences
Machine learning
MEMS
microcontroller
Micromechanical devices
neural network
Prediction algorithms
roller bearing
Rolling bearings
vibration
Vibrations
Online AccessGet full text

Cover

More Information
Summary:This work implements an in-house fabricated, cost-effective MEMS piezoelectric accelerometer module to acquire the vibration data for fault diagnosis of a rolling bearing in an electric motor. Choosing the right combination of data pre-processing method (Ensemble Empirical Mode Decomposition, EEMD) and machine learning model (Back Propagation Artificial Neural Network, BP-ANN) offers 99.61% accuracy in detecting the location and state of various bearing faults under varying motor speeds of 500, 1000, and 1500rpm, with fewer pre-processing steps. The developed module features excellent compatibility with industrial 4.0 intelligent manufacturing applications.
ISSN:2160-1968
DOI:10.1109/MEMS51670.2022.9699529