Bearing Prognostics: An Instance-Based Learning Approach with Feature Engineering, Data Augmentation, and Similarity Evaluation

Jun Sun,Qiao Sun

doi:10.3390/signals2040040

Abstract

We propose an instance-based learning approach with data augmentation and similarity evaluation to estimate the remaining useful life (RUL) of a mechanical component for health management. The publicly available PRONOSTIA datasets, which provide accelerated degradation test data for bearings, are used in our study. The challenges with the datasets include a very limited number of run-to-failure examples, no failure mode information, and a wide range of bearing life spans. Without a large number of training samples, feature engineering is necessary. Principal component analysis is applied to the spectrogram of vibration signals to obtain prognostic feature sequences. A data augmentation strategy is developed to generate synthetic prognostic feature sequences using learning instances. Subsequently, similarities between the test and learning instances can be assessed using a root mean squared (RMS) difference measure. Finally, an ensemble method is developed to aggregate the RUL estimates based on multiple similar prognostic feature sequences. The proposed approach demonstrates comparable performance with published solutions in the literature. It serves as an alternative method for solving the RUL estimation problem.

Highlights

IntroductionDao. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
We described the proposed instance-based learning (IBL) approach step by step with the example of the test instance Bearing1-3 and the learning instance Bearing1-1 in the PRONOSTIA bearing datasets
We can observe that the proposed IBL approach can achieve a comparable performance with the eight representative solutions to the challenging remaining useful life (RUL)

Summary

Introduction

Dao. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. An important task of prognostics and health management (PHM) is to estimate the remaining useful life (RUL). It is the time to failure if the monitored system continues to operate without intervention. RUL estimation is tackled by first extracting prognostic features from data collected as part of the monitoring process to represent the degradation state. A prognostic model is developed based on prognostic features and used to generate RUL estimates

Methods

Results

Conclusion