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

arXiv: https://arxiv.org/abs/1703.06272 · HTML: https://mlsanity.com/paper/1703.06272/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2017.

## Built on by (most cited first, 3 of 3)

- [Online Detection of Vibration Anomalies Using Balanced Spiking Neural Networks](https://mlsanity.com/paper/2106.00687/) (2021)
- [Bearings Fault Detection Using Hidden Markov Models and Principal Component Analysis Enhanced Features](https://mlsanity.com/paper/2104.10519/) (2021)
- [Quantum Machine Learning for Health State Diagnosis and Prognostics](https://mlsanity.com/paper/2108.12265/) (2021)

## Abstract (arXiv)

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.