An Automated Auto-encoder Correlation-based Health-Monitoring and Prognostic Method for Machine Bearings

TL;DR

This paper introduces an automated auto-encoder correlation method for health monitoring and prognostics of machine bearings, utilizing vibration data and unsupervised feature extraction to accurately detect degradation.

Contribution

The paper presents a novel auto-encoder correlation-based technique that autonomously extracts features and effectively monitors bearing health, outperforming existing methods.

Findings

AEC method accurately detects degradation onset

Method generalizes across multiple run-to-failure tests

Outperforms state-of-the-art health monitoring approaches

Abstract

This paper studies an intelligent ultimate technique for health-monitoring and prognostic of common rotary machine components, particularly bearings. During a run-to-failure experiment, rich unsupervised features from vibration sensory data are extracted by a trained sparse auto-encoder. Then, the correlation of the extracted attributes of the initial samples (presumably healthy at the beginning of the test) with the succeeding samples is calculated and passed through a moving-average filter. The normalized output is named auto-encoder correlation-based (AEC) rate which stands for an informative attribute of the system depicting its health status and precisely identifying the degradation starting point. We show that AEC technique well-generalizes in several run-to-failure tests. AEC collects rich unsupervised features form the vibration data fully autonomous. We demonstrate the superiority of the AEC over many other state-of-the-art approaches for the health monitoring and prognostic of machine bearings.