A deep learning approach towards diagnostics of bearings operating under non-stationary conditions

A deep learning approach towards diagnostics of bearings operating under non-stationary conditions

Faults in bearings usually manifest as marginal defects that intensify over time, allowing for well-informed preventative actions with early Fault Detection and Diagnosis (FDD) protocols. Detection of the fault begins with capturing, for example, acceleration signals from a machine. Traditionally, h...

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Bibliographic Details
Main Author: Baggerohr, Stephan
Other Authors: Heyns, P.S. (Philippus Stephanus)
Language:en_US
Published: University of Pretoria 2020
Subjects:
UCTD
Online Access:http://hdl.handle.net/2263/73452
Baggerohr, S 2019, A deep learning approach towards diagnostics of bearings operating under non-stationary conditions, MEng Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/73452>

Internet

http://hdl.handle.net/2263/73452
Baggerohr, S 2019, A deep learning approach towards diagnostics of bearings operating under non-stationary conditions, MEng Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/73452>

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