Degradation Data–Driven Analysis for Estimation of the Remaining Useful Life of a Motor

Abstract

Highly dynamic loading conditions on clutch motors used in four-wheeled passenger vehicles cause them to fail quite often. The current diagnostic tools have proven to be inefficient to detect the onset of system degradation. This paper presents a degradation model to exhibit the state of health of the clutch. A novel condition indicator (CI) and a threshold for conditionally independent noisy signal from the motor subjected to cumulative degradation have been established. A dominating feature characterizing the motor health was discerned to be spectral entropy kurtosis which was identified while analyzing the time-series signal composed of agglomeration of different frequencies that produce higher octaves. Tests for monotonocity and trendability metrics affirmed that spectral entropy kurtosis is a distinguishing CI. Principal component analysis (PCA) allowed the fusion of features for the selection of the best-performing CI. The proposed CI was used in an exponential degradation model to predict the remaining useful life (RUL) of the motor with improved accuracy.