Pattern Recognition of Bearing Faults using Smoother Statistical Features

03/15/2015 ∙ by Muhammad Masood Tahir, et al. ∙ 0 ∙ share

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.

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Pattern Recognition of Bearing Faults using Smoother Statistical Features

Authors

Separable and non-separable data representation for pattern discrimination

Machine Learning pipeline for discovering neuroimaging-based biomarkers in neurology and psychiatry

Randomized Computer Vision Approaches for Pattern Recognition in Timepix and Timepix3 Detectors

Proceedings of The 39th Annual Workshop of the Austrian Association for Pattern Recognition (OAGM), 2015

Developing Autonomic Properties for Distributed Pattern-Recognition Systems with ASSL: A Distributed MARF Case Study

Graph Quantization

fMRI: preprocessing, classification and pattern recognition

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