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Pattern Recognition of Bearing Faults using Smoother Statistical Features

Computer Vision and Pattern Recognition 2015-12-01 v2
Authors: Muhammad Masood Tahir , Ayyaz Hussain
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Abstract

A pattern recognition (PR) based diagnostic scheme is presented to identify bearing faults, using time domain features. Vibration data is acquired from faulty bearings using a test rig. The features are extracted from the data, and processed prior to utilize in the PR process. The processing involves smoothing of feature distributions. This reduces the undesired impact of vibration randomness on the PR process, and thus enhances the diagnostic accuracy of the model.

Cite

@article{arxiv.1503.04444,
  title  = {Pattern Recognition of Bearing Faults using Smoother Statistical Features},
  author = {Muhammad Masood Tahir and Ayyaz Hussain},
  journal= {arXiv preprint arXiv:1503.04444},
  year   = {2015}
}

Comments

This paper has been withdrawn by the author due to a crucial errors

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