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This paper studies an intelligent ultimate technique for health-monitoringand prognostic of common rotary machine components, particularly bearings.During a run-to-failure experiment, rich unsupervised features from vibrationsensory data are extracted by a trained sparse auto-encoder. Then, thecorrelation of the extracted attributes of the initial samples (presumablyhealthy at the beginning of the test) with the succeeding samples is calculatedand passed through a moving-average filter. The normalized output is namedauto-encoder correlation-based (AEC) rate which stands for an informativeattribute of the system depicting its health status and precisely identifyingthe degradation starting point. We show that AEC technique well-generalizes inseveral run-to-failure tests. AEC collects rich unsupervised features form thevibration data fully autonomous. We demonstrate the superiority of the AEC overmany other state-of-the-art approaches for the health monitoring and prognosticof machine bearings.

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