Title: An Automated Auto-encoder Correlation-based Health-Monitoring and Prognostic Method for Machine Bearings,
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.