Skip to main content
padlock icon - secure page this page is secure

Improved bearing sensing for prognostics: from vibrations to optical fibres

Buy Article:

$22.00 + tax (Refund Policy)

Bearings are vital elements in rotating machinery. Despite their wide use, bearings are prone to failures that can result in irreversible damage. The detection of bearing damage in its incipient stages and monitoring of the fault severity are required for the optimisation of maintenance decisions. The classical methods for bearing monitoring are based on an analysis of the vibration signals usually captured by an accelerometer located on the machine case. Two difficulties arise when diagnosing bearings via vibrations: the effect of the transmission path to the sensor distorts the signals and the low signal-to-noise ratio characterising the weak bearing signals in the presence of the accompanying strong surrounding noise originating from the vibrations of other rotating components in the machine.

The goal of the presented work was to study the possibility of overcoming the current problems of bearing prognostics by locating the sensors as close as possible to the bearing. Two types of sensor were selected: micro electro-mechanical system (MEMS) accelerometers and optical fibres to measure strain. Due to their small dimensions, these sensors can be embedded into the system close to, or even inside, the bearing. The measured signals from bearings with various spall widths show an improved signal-to-noise ratio, demonstrating the power of these two local sensing methodologies. By avoiding transmission path effects, the results show the clear detection of early defects.

The results of this study open new options for monitoring and detecting the early signs of failure in critical bearings.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Document Type: Research Article

Publication date: August 1, 2015

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more