Prognostics of Induction Motor Shaft Based on Feature Importance and Least Square Support Vector Machine Regression

  • Didik Djoko Susilo Mechanical Engineering Department of Sebelas Maret University
  • Achmad Widodo Diponegoro University
  • Toni Prahasto Diponegoro University
  • Muhammad Nizam Sebelas Maret University
Keywords: Induction motor shaft; feature importance; LSSVM regression; RUL estimation

Abstract

This paper aims to present a prognostic method for induction motor shafts that experience fatigue failure in the keyway area, using motor vibration signals. Preprocessing the data to eliminate noise in raw signals is done by decomposing the signal, using discrete wavelet transforms. Prognostic indicator candidates are obtained through the selection of features based on its importance, which involve the superposition of monotonicity and trendability parameters. The prognostics model is built based on the least squares support vector machine regression approach. Remaining useful life (RUL) estimates of motor shafts were performed by fitting the sum of two exponential functions to the regression results and extrapolating over time until the specified failure threshold hits. The results of the study show that the proposed method can work satisfactorily to estimate the RUL of motor shaft. The best prognostic indicator namely the RMS, can be used to predict the motor shaft RUL since 50% of the time step before the end of the motor shaft life is error bound within 20%.

Author Biographies

Achmad Widodo, Diponegoro University

Mechanical Engineering Department

Toni Prahasto, Diponegoro University

Mechanical Engineering Department

Muhammad Nizam, Sebelas Maret University

Electrical Engineering Department

Published
2021-03-05
How to Cite
Susilo, D., Widodo, A., Prahasto, T., & Nizam, M. (2021). Prognostics of Induction Motor Shaft Based on Feature Importance and Least Square Support Vector Machine Regression. International Journal of Automotive and Mechanical Engineering, 18(1), 8464 -. https://doi.org/10.15282/ijame.18.1.2021.06.0641
Section
Articles