modeling the relationship between vibration features and condition parameters using relevance vector machines for health monitoring of rolling element bearings under varying operation conditions

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ID: 152105
2015
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Abstract
Rotational speed and load usually change when rotating machinery works. Both this kind of changing operational conditions and machine fault could make the mechanical vibration characteristics change. Therefore, effective health monitoring method for rotating machinery must be able to adjust during the change of operational conditions. This paper presents an adaptive threshold model for the health monitoring of bearings under changing operational conditions. Relevance vector machines (RVMs) are used for regression of the relationships between the adaptive parameters of the threshold model and the statistical characteristics of vibration features. The adaptive threshold model is constructed based on these relationships. The health status of bearings can be indicated via detecting whether vibration features exceed the adaptive threshold. This method is validated on bearings running at changing speeds. The monitoring results show that this method is effective as long as the rotational speed is higher than a relative small value.
Reference Key
hu2015mathematicalmodeling Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Lei Hu;Niao-qing Hu;Bin Fan;Feng-shou Gu;Xiang-yi Zhang
Journal journal of power sources
Year 2015
DOI
10.1155/2015/123730
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