Name
Abstract | ||
|---|---|---|
Incipient fault detection in low signal-to-noise ratio (SNR) conditions requires robust features for accurate condition-based machine health monitoring. Accurate fault classification is positively linked to the quality of features of the faults. Therefore, there is a need to enhance the quality of the features before classification. This paper presents a novel vibration spectrum imaging (VSI) feature enhancement procedure for low SNR conditions. An artificial neural network (ANN) has been used as a fault classifier using these enhanced features of the faults. The normalized amplitudes of spectral contents of the quasi-stationary time vibration signals are transformed into spectral images. A 2-D averaging filter and binary image conversion, with appropriate threshold selection, are used to filter and enhance the images for the training and testing of the ANN classifier. The proposed novel VSI augments and provides the visual representation of the characteristic vibration spectral features in an image form. This provides enhanced spectral images for ANN training and thus leads to a highly robust fault classifier. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TIE.2014.2327555 | Industrial Electronics, IEEE Transactions |
Keywords | DocType | Volume |
condition monitoring,fault diagnosis,filtering theory,image classification,image enhancement,image representation,image segmentation,learning (artificial intelligence),machine bearings,mechanical engineering computing,neural nets,spectral analysis,vibrations,2-D averaging filter,ANN fault classifier,ANN training,SNR conditions,VSI,artificial neural network,bearing fault classification approach,binary image conversion,characteristic vibration spectral features,condition-based machine health monitoring,incipient fault detection,normalized amplitudes,quasistationary time vibration signals,robust fault classifier,robust features,signal-to-noise ratio conditions,spectral contents,spectral images,vibration spectrum imaging feature enhancement procedure,visual representation,Artificial neural networks (ANNs),bearing fault,fault diagnosis,image processing,machine health monitoring (MHM) | Journal | 62 |
Issue | ISSN | Citations |
1 | 0278-0046 | 2 |
PageRank | References | Authors |
0.37 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Amar | 1 | 6 | 1.14 |
| Iqbal Gondal | 2 | 316 | 48.05 |
| Campbell Wilson | 3 | 23 | 6.62 |