Classification of fault diagnosis in a gear wheel by used probabilistic neural network, fast Fourier transform and principal component analysis
Classification of fault diagnosis in a gear wheel by used probabilistic neural network, fast Fourier transform and principal component analysis
- New search for: Piotr CZECH
- New search for: Piotr CZECH
This paper presents the results of an experimental application of artificial neural network as a classifier of the degree of cracking of a tooth root in a gear wheel. The neural classifier was based on the artificial neural network of Probabilistic Neural Network type (PNN). The input data for the classifier was in a form of matrix composedof statistical measures, obtained from fast Fourier transform (FFT) and principal component analysis (PCA). The identified model of toothed gear transmission, operating in a circulating power system, served for generation of the teaching and testing set applied for the experiment.
Document information
Classification of fault diagnosis in a gear wheel by used probabilistic neural network, fast Fourier transform and principal component analysis
2007
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Similar titles
Fault diagnosis in internal combustion engines using Principal Component Analysis
| Automotive engineering | 2003
Principal component algorithm for sero-engine fault diagnosis
| British Library Online Contents | 1998
Fast Protein Superfamily Classification Using Principal Component Null Space Analysis
| British Library Conference Proceedings | 2005
| British Library Online Contents | 1994